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Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review

López Fernández, Aurelio; Gómez-Vela, Francisco A.; Rodríguez–Baena, Domingo S.; Delgado-Chaves, Fernando M.; Gonzalez‑Dominguez, Jorge · 2025

Abstract

Biclustering is a powerful machine learning technique that simultaneously groups rows and columns in matrix-based datasets. Applied to gene expression data in bio informatics, its use has expanded alongside the rapid growth of high-throughput sequencing technologies, leading to massive and complex biological datasets. This review aims to examine how biclustering methods and their validation strategies are evolving to meet the demands of High Performance Computing (HPC) and Big Data environments. We present a structured classification of existing approaches based on the computational paradigms they employ, including MPI/OpenMP, Apache Hadoop/Spark, and GPU/CUDA. By synthesising these developments, we highlight current trends and outline key research challenges. The knowledge gathered in this work may support researchers in adapting and scaling biclustering algorithms to analyse large-scale biomedical data more efficiently. Our contribution is intended to bridge the gap between algorithmic innovation and computational scalability in the context of bioinformatics and data-intensive applications.

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Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review

Author: López Fernández, Aurelio; Gómez-Vela, Francisco A.; Rodríguez–Baena, Domingo S.; Delgado-Chaves, Fernando M.; Gonzalez‑Dominguez, Jorge
Publisher: Springer
Year: 2025
DOI: 10.1007/s11227-025-07563-6
Source: https://idus.us.es/bitstreams/0cac273e-087b-417c-8ba9-92ee9898f4b1/download
Vol.:(0123456789)
The Jou nal o Supe compu ing (2025) 81:1123
h ps://doi.o g/10.1007/s11227-025-07563-6
Biclus e ing inbioin o ma ics using big da a andHigh
Pe o mance Compu ing applica ions: challenges
andpe spec i es, a e iew
Au elioLópez‑Fe nández4· F anciscoA.Gomez‑Vela1·
DomingoS.Rod iguez‑Baena1· Fe nandoM.Delgado‑Cha es2·
Jo geGonzalez‑Dominguez3
Accep ed: 6 June 2025
© The Au ho (s) 2025
Abs ac
Biclus e ing is a powe ul machine lea ning echnique ha simul aneously g oups
ows and columns in ma ix-based da ase s. Applied o gene exp ession da a in bio-
in o ma ics, i s use has expanded alongside he apid g ow h o high- h oughpu
sequencing echnologies, leading o massi e and complex biological da ase s. This
e iew aims o examine how biclus e ing me hods and hei alida ion s a egies a e
e ol ing o mee he demands o High Pe o mance Compu ing (HPC) and Big Da a
en i onmen s. We p esen a s uc u ed classi ica ion o exis ing app oaches based
on he compu a ional pa adigms hey employ, including MPI/OpenMP, Apache
Hadoop/Spa k, and GPU/CUDA. By syn hesising hese de elopmen s, we highligh
cu en ends and ou line key esea ch challenges. The knowledge ga he ed in his
wo k may suppo esea che s in adap ing and scaling biclus e ing algo i hms o
analyse la ge-scale biomedical da a mo e e icien ly. Ou con ibu ion is in ended o
b idge he gap be ween algo i hmic inno a ion and compu a ional scalabili y in he
con ex o bioin o ma ics and da a-in ensi e applica ions.
Keywo ds Biclus e ing· Big da a· High Pe o mance Compu ing· Bioin o ma ics
Abb e ia ions
ACV A e age co ela ion alue
AWS Amazon web se ices
CC Cheng–Chu ch
CCS The condi ion-dependen co ela ion subg oups
CPU Cen al p ocessing uni
CUDA Compu e uni ied de ice a chi ec u e
EBI Eu opean bioin o ma ics ins i u e
EBIC E olu iona y-based bIClus e ing
ENCODE Encyclopedia o DNA elemen s
Ex ended au ho in o ma ion a ailable on he las page o he a icle
A.López-Fe nández e al.
1123 Page 2 o 52
FCA Fo mal concep analysis
FLOC FLexible o e lapped biClus e ing
FPGA Field-p og ammable ga e a ay
GB Gigaby e
GBC Geome ic biclus e ing algo i hm
GMQL GenoMe ic que y language
GPGPU Gene al-pu pose compu ing on g aphics p ocessing uni s
GPU G aphics p ocessing uni
GTEX Geno ype- issue exp ession p ojec
HDFS Hadoop dis ibu ed ile sys em
HPC High Pe o mance Compu ing
KEGG Kyo o encyclopedia o genes and genomes
LCS Longes common sequence
MATLAB MAT ix labo a o y
MCC Ma hews co ela ion coe icien
MPI Message passing in e ace
MR MapReduce
MSR Mean squa e esidue
NGS Nex -gene a ion sequencing
NMF Non-nega i e ma ix ac o isa ion
NMI No malised mu ual in o ma ion
PBD-SPEA2 Pa allel biclus e ing de ec ion
PC Pa allel coo dina e
PCR Polyme ase chain eac ion
PGAS Pa i ioned global add ess space
POSIX Po able ope a ing sys em in e ace
PPI P o ein–p o ein in e ac ion
RAM Random access memo y
RDD Robus dis ibu ed da ase s
RNA Ribonucleic acid
SM NVIDIA S eaming mul i-p ocesso
SMP Sha ed memo y pa allelism
SPEA2 S eng h pa e o on e olu iona y algo i hm2
SPMD Single p og am, mul iple da a
SPUs S eam p ocesso uni s
SPs S eaming p ocesso s
SQL S uc u ed que y language
STRING Sea ch ool o he e ie al o in e ac ing genes/p o ein
TCGA The cance genome a las
UM Uni ied memo y
Biclus e ing inbioin o ma ics using big da a andHigh… Page 3 o 52 1123
1 In oduc ion
Biclus e ing echniques ha e been success ully applied o se e al esea ch a eas,
including ene gy consump ion [1], economics [2], ading o ecas ing [3], ma ke -
ing [4], o ecommenda ion sys ems [5]. In bioin o ma ics, hese echniques a e
o en used o applica ions such as analysing gene exp ession da a, disco e ing and
anno a ing new unc ionali ies o unclassi ied genes, inding exp ession modules,
econs uc ing biological ne wo ks, elucida ing disease mechanisms, and s a i ying
pa ien s [6, 7]. The applica ion o biclus e ing echniques on gene exp ession da a-
se s has wo main ad an ages o e clus e ing: (a) i g oups bo h genes and expe i-
men al condi ions, which is much close o biology since a subse o genes may ha e
common biological beha iou only unde a subse o expe imen al condi ions o
samples [8], and (b) i conside s g oup o e lapping, allowing genes o con ibu e o
mo e han one biological ac i i y. Despi e hese ad an ages, se e al challenges a ise,
mainly due o he exponen ial g ow h o biological da ase s—in bo h olume and
complexi y—gene a ed by nex -gene a ion sequencing (NGS) echnologies [9] and
he c ea ion o la ge-scale genomic conso iums [10].
The comple e anno a ion and quan i ica ion o he exp ession le el o all genes
and hei iso o ms in a speci ic sample [11], along wi h he da a olume ha ep-
esen s gene sequencing in o ma ion om 100 million o 2 billion indi idual
pa ien s in he yea 2025 [10], demons a e some challenges aced in his ield.
Also, i is essen ial o conside ha his da a may o igina e om a ious sou ces,
be s o ed in se e al o ma s, and ypically con ain noise and a e ac s ha equi e
esolu ion [12]. This inc eases he compu a ional complexi y o biclus e ing
me hods used o e alua e and ex ac meaning ul in o ma ion om la ge biomedi-
cal and biological da ase s.
To add ess hese challenges, High Pe o mance Compu ing (HPC) app oaches,
such as Gene al-Pu pose Compu ing on G aphics P ocessing Uni s (GPGPU),
Big Da a amewo ks, and adi ional pa allel and dis ibu ed compu ing pa a-
digms, ha e been adop ed o imp o e pe o mance and scalabili y [13]. Despi e
ha ing p o en highly use ul in mul iple con ex s, con en ional biclus e ing ech-
niques exhibi a numbe o echnical and compu a ional limi a ions when dealing
wi h ex emely la ge da ase s. These limi a ions can be summa ised as ollows:
• High compu a ional complexi y: Con en ional echniques o en aim o ind
op imal solu ions a he cos o high compu a ional complexi y. This becomes
unsus ainable when inpu ma ices con ain millions o ows and columns. Fo
example, he i s ue biclus e ing algo i hm, he Cheng–Chu ch algo i hm
[14], has app oxima ely quad a ic complexi y in he numbe o ows and col-
umns, making i poo ly scalable.
• Memo y limi a ions: Many algo i hms s o e en i e ma ices o mul iple auxil-
ia y s uc u es in memo y, which becomes un easible wi h e y la ge da ase s.
Fo ins ance, algo i hms like SAMBA [15] may encoun e memo y manage-
men issues when handling gene exp ession da ase s wi h mo e han 100,000
genes and condi ions.
A.López-Fe nández e al.
1123 Page 4 o 52
• Sensi i i y o noise and edundancy: In la ge da ase s, noise is ine i able
and can lead o many spu ious pa e ns. Classical echniques a e no always
designed o e ec i ely il e ou his noise. Algo i hms such as Bimax [16]
sea ch o exac biclus e s (i.e. noise- ee), which makes hem less use ul o
la ge-scale eal-wo ld da a.
In addi ion o hese h ee main issues, he e is also a lack o adap abili y o dis-
ibu ed s uc u es and an inabili y o handle he e ogeneous o dynamic da a.
The impo ance o s udying biclus e ing unde he Big Da a and HPC pa a-
digms lies in he g owing need o ex ac biologically meaning ul pa e ns om
massi e and complex da ase s. Fo example, in he wo k p esen ed by Liu e al.
[17], i e gene exp ession da ase s we e p ocessed, gene a ing a o al o 8280536
biclus e s. F om a genomic conso ium poin o iew, ano he example can be
ound in The Cance Genome A las (TCGA) [18], which p o ides mul i-omics
da a o housands o umou samples ac oss nume ous cance ypes. The iden-
i ica ion o biclus e s in his con ex can help disco e cance -speci ic gene
exp ession modules, s a i y pa ien s based on molecula p o iles, and suppo he
design o a ge ed he apies. Howe e , he olume and complexi y o hese ypes
o da ase means ha adi ional biclus e ing me hods a e insu icien . So a sys-
ema ic examina ion o new biclus e ing app oaches ha in eg a e obus and e i-
cien compu a ional s a egies wi h biological in e p e abili y is equi ed.
Since he publica ion o he wo k by Sa a C. Madei a e al. [19], many s ud-
ies ha e been p esen ed ecen ly whose objec i e is o p esen a classi ica ion o
compa a i e s udy. Despi e his weal h o in o ma ion, o he bes o ou knowl-
edge, none o hese pape s has ocused hei s udy on how new o e en exis ing
biclus e ing echniques ha e me he challenge o being able o adap o a Big
Da a o HPC ecosys em o p ocess he biological and biomedical da ase s ha a e
cu en ly a ailable. The e is also no ela ed wo k on he abili y o biclus e ing
echniques o alida e he la ge numbe o esul s hey gene a e o how biclus e
alida ion me hods add ess his p oblem. Mo eo e , in o de o cope wi h a Big
Da a ecosys em, hese echniques should also ake in o accoun he a ious chal-
lenges hey mus ace when ying o adap o some speci ic HPC me hods. How-
e e , he e a e also no wo ks ha add ess he challenges in ol ed in his ype o
adap a ion.
This pape p esen s an o e iew o esea che s and p ac i ione s o disco e sui -
able biclus e ing solu ions o la ge-scale bioin o ma ics applica ions while p omo -
ing u u e esea ch ha connec s biological knowledge wi h compu a ional scalabil-
i y. This wo k explo es he e olu ion o biclus e ing echniques in esponse o he
inc easing scale and complexi y o bioin o ma ic da ase s. I examines he inco po-
a ion o High Pe o mance Compu ing (HPC) and Big Da a amewo ks as key ena-
ble s o imp o ing he scalabili y and applicabili y o hese me hods in eal-wo ld
biomedical scena ios. Fu he mo e, i analyses he p incipal limi a ions and ongoing
challenges ha pe sis in his in e disciplina y ield, highligh ing he need o con in-
ued me hodological inno a ion o ensu e bo h compu a ional e iciency and biologi-
cal in e p e abili y. The e o e, we can summa ise he con ibu ions o his wo k as
ollows:
Biclus e ing inbioin o ma ics using big da a andHigh… Page 5 o 52 1123
• A sys ema ic and up- o-da e su ey o biclus e ing echniques ha add ess he
challenges posed by la ge-scale biomedical da a.
• A classi ica ion o hese me hods based on hei compu a ional pa adigms and
p ac ical implemen a ions.
• A c i ical analysis o hei biological applica ions and he epo ed compu a ional
pe o mance based on a comp ehensi e e iew o he exis ing li e a u e.
• A summa y o cu en challenges and u u e di ec ions in his ield.
The s uc u e o he es o he pape is as ollows: Sec ion3 p esen s a lis o he
mos ele an biclus e ing e iews. Sec ion4 g oups he p oblems ha HPC and
Big Da a applica ions used by biclus e ing echniques mus add ess o suppo huge
da ase s and ob ain hei esul s in he sho es possible ime. Biclus e ing echniques
based on Big Da a and HPC applica ions a e examined in Sec ion 5. Sec ion 6
e iews he di e en biclus e alida ion me hods and alida ion p ocesses imple-
men ed in biclus e ing echniques ha can alida e la ge amoun s o esul s in he
sho es possible execu ion ime. Sec ion7 desc ibes he challenges ha biclus e ing
and i s alida ion measu es mus ace o adap o a Big Da a ecosys em. Finally, Sec-
ion8 summa ises he main conclusions de i ed om his wo k.
2 Re iew me hodology
This s udy employs a s uc u ed p ocess based on he PRISMA 2020 p inciples o
gua an ee scien i ic igou , anspa ency, and epea abili y. Two dis inc ye in e con-
nec ed analyses we e conduc ed o encapsula e he ecen ad ancemen s in biclus e -
ing me hodologies employed in bioin o ma ics, pa icula ly in ligh o Big Da a and
high-pe o mance compu ing issues.
The ini ial s udy concen a ed on disco e ing e iew a icles and su eys ha
in es iga e biclus e ing echniques used in bioin o ma ics. The aim was o e alua e
he dep h, b ead h, and emphasis o p e ious e alua ions, speci ically wi h hei han-
dling o scalabili y, alida ion, and p ac ical use ulness in bioin o ma ics.
The second s udy ocused on o iginal esea ch a icles ha p oposed o imple-
men ed biclus e ing me hods inside High Pe o mance Compu ing (HPC) and Big
Da a en i onmen s. This encompasses biclus e ing me hods using adi ional pa al-
lel and dis ibu ed models (e.g. MPI, OpenMP), Big Da a amewo ks (e.g. Apache
Spa k, Hadoop), and GPGPU accele a ion (e.g. CUDA, OpenCL). The objec i e is
o delinea e ends, p oblems, and he echnological de iciencies in scalable biclus e
deploymen s and hei applica ions wi hin he bioin o ma ics domain.
2.1 Sea ch s a egy andda a sou ces
Bo h analyses employed sys ema ic sea ch s a egies ac oss h ee majo da abases:
Scopus, PubMed, and Google Schola . The sea ches we e ca ied ou in May 2025
and we e limi ed o pee - e iewed a icles published be ween 1 Janua y 2009 and 25
May 2025. Only a icles w i en in English we e included.

A.López-Fe nández e al.
1123 Page 6 o 52
Fo he e iew and su ey wo ks, que ies combined e ms such as: ”biclus e ing”,
”co-clus e ing”, ”su ey”, ” e iew”, ”bioin o ma ics”, and ”gene exp ession”.
Fo he HPC/Big Da a implemen a ions, que ies included combina ions o :
”biclus e ing”, ”co-clus e ing”, ”pa allel compu ing”, ”dis ibu ed compu ing”,
”high-pe o mance compu ing”, ”HPC”, ”Big Da a”, ”Apache Spa k”, ”Hadoop”,
”GPU”, ”GPGPU”, ”CUDA”, and ”bioin o ma ics”.
In all cases, Boolean ope a o s and pla o m-speci ic syn ax we e used. Al hough
all analyses we e pe o med o all pla o ms, some illus a i e que ies o each da a-
base a e shown below.
PubMed que y (su ey and e iew iden i ica ion):
((biclus e ing[Ti le/Abs ac ] OR ”co-
clus e ing”[Ti le/Abs ac ]) AND ( e iew[Ti le/
Abs ac ] OR su ey[Ti le/Abs ac ] OR
benchma k*[Ti le/Abs ac ]) AND (bioin o ma ics[MeSH
Te ms] OR ”gene exp ession”[Ti le/Abs ac ])) AND
(”2009/01/01”[Da e - Publica ion] : ”3000”[Da e -
Publica ion])
Scopus que y (Big Da a-based biclus e ing algo i hms):
(TITLE-ABS-KEY(biclus e ing OR ”co-clus e ing”)
AND TITLE-ABS-KEY(”Big Da a” OR ”Spa k” OR ”Apache
Spa k” OR ”Hadoop” OR ”Apache Hadoop” OR ”MapReduce”
OR ”Map-Reduce”) AND TITLE-ABS-KEY(bioin o ma ics OR
”gene exp ession”)) AND PUBYEAR> 2008
Google Schola que y (GPU-based biclus e ing algo i hms):
”biclus e ing” AND (”GPU” OR ”CUDA” OR ”GPGPU” OR
”OpenCL”) AND (bioin o ma ics OR ”gene exp ession”)
- e iew -su ey a e :2008
As can be seen, each que y was ca e ully c a ed o ma ch he syn ax and index-
ing s a egies o he co esponding pla o m. Ad anced il e s (e.g. publica ion yea
ange, language, pee - e iewed jou nals) we e also applied when a ailable.
2.2 Eligibili y c i e ia andsc eening p ocess
A icles we e managed using Zo e o, which enabled deduplica ion and s uc u ed
agging. The selec ion was ca ied ou in wo s ages: (1) i le and abs ac sc eening
and (2) ull- ex e alua ion. Sepa a e inclusion/exclusion c i e ia we e de ined o
each analysis.
Fo su ey analysis, s udies we e included i hey we e published e iews o su -
eys ha analysed biclus e ing echniques applied speci ically o biological da a.
P io i y was gi en o wo k ocused on bioin o ma ics o omics da ase s, pa icula ly
hose in ol ing gene exp ession analysis. S udies we e excluded i hey did no ela e
Biclus e ing inbioin o ma ics using big da a andHigh… Page 7 o 52 1123
o biological applica ions—such as e iews cen ed on ma ke ing o social ne wo k
da a—o i hey lacked pee - e iewed s a us (e.g. edi o ials o in o mal o e iews).
Fo he analysis o he HPC/Big Da a biclus e ing algo i hm analysis, a icles
we e included i hey p oposed o applied biclus e ing echniques wi hin pa al-
lel o dis ibu ed compu ing en i onmen s, including implemen a ions using GPU
o GPGPU accele a ion. Wo ks ha desc ibed applica ions wi hin Big Da a ame-
wo ks such as Apache Spa k o Hadoop we e also eligible. The s udies ha we e no
included in his analysis we e hose ha did no ha e any compu a ional implemen-
a ion, did no ocus on scalabili y o pe o mance, o only used simple oy da ase s
ha we e no ele an o p ocessing la ge-scale biological da a.
The i s analysis yielded 490 candida e e iew a icles, o which 24 me all inclu-
sion c i e ia. The second analysis e ie ed 432 implemen a ion a icles, o which 24
we e selec ed a e ull- ex sc eening.
2.3 Da a ex ac ion andclassi ica ion
Fo bo h analyses, me ada a we e ex ac ed, including publica ion yea , ci a ion
coun ( ia Scopus/Google Schola ), jou nal qua ile, a ge o ganism (i applicable),
da a ype (e.g. mic oa ay, RNA-Seq), compu a ional pla o m (e.g. Spa k, MPI,
GPU), and e alua ion me ics.
S udies om he second analysis we e u he ca ego ised by compu a ional
pa adigm:
• Pa allel and dis ibu ed compu ing (e.g. MPI, OpenMP)
• Big Da a amewo ks (e.g. Spa k, Hadoop)
• GPU-based app oaches (e.g. CUDA, OpenCL)
This classi ica ion enabled a compa a i e syn hesis o algo i hmic s a egies and
implemen a ion pa e ns.
2.4 Re iew scope and ype
This wo k combines bo h sys ema ic and na a i e e iew elemen s. The sys em-
a ic componen s p o ide a ep oducible s uc u e and empi ical ounda ion, while
he na a i e analysis enables c i ical in e p e a ion o ends, bo lenecks, and me h-
odological gaps. This hyb id app oach allows o bo h e idence-based syn hesis and
o wa d-looking insigh .
2.5 E idence o in e na ional in e es and ele ance
In bo h analyses, he a icles included co e mo e han 20 coun ies, wi h s ong ep-
esen a ion om ins i u ions in he USA, China, Ge many, India, and Spain. App ox-
ima ely 74% o he selec ed s udies we e published in jou nals anked in Q1/Q2 in
hei ca ego y acco ding o he Scimago Jou nal Rank (SJR). I is also in e es ing o
no e ha o hese publica ions anked in Q1, he selec ed su eys exceed he 80%
A.López-Fe nández e al.
1123 Page 8 o 52
h eshold, while o HPC biclus e ing algo i hms hey s and a 67%. Ci a ion me ics
also suppo he ele ance o his ield: he a e age ci a ion coun was 47.3, wi h a
median o 31. These indica o s ein o ce he g owing in e na ional in e es in scal-
able biclus e ing echniques in biomedical esea ch.
3 Rela ed wo k
Nume ous biclus e ing e iews a e a ailable in he li e a u e, wi h he mos el-
e an ones de ailed in Table2 o Appendix A. This able includes publica ion yea ,
au ho s, p incipal applica ion, and i s scope: biclus e ing echniques (me hods) and
alida ion echniques ( alida ion). Re iews can span mul iple ca ego ies, as he
g oups a e no mu ually exclusi e. Rega ding biclus e ing s a egies, S. Busygin
e al. [20] o e an ex ensi e analysis o he ma hema ical p inciples in ol ed in he
sea ch o biclus e s by means o a ious echniques ha a e classi ied based on
hei applica ion domains. B. Pon es e al. [21] classi y algo i hms in o me ic and
non-me ic g oups, employing di e se e alua ion me ics such as i e a i e g eedy
sea ch, me a-heu is ics, clus e ing-based, p obabilis ic models, and linea algeb a.
Recen wo ks ha e been published ha p o ide a mo e comp ehensi e analysis, such
as he s udy by A. José-Ga cía e al. [22], which speci ically examines biclus e -
ing algo i hms ha use me a-heu is ics as e alua ion me ics. Conce ning e iews o
biclus e alida ion echniques, Tanay e al. [23] examine sco ing model in luence in
he de ec ion o signi ican biclus e s, while San ama ia e al. [24] p opose me ics
o illus a e he consis ency and e icacy o biclus e ing echniques o ge biological
in o ma ion in he p esence o noise exposu e and o e lap be ween biclus e s.
Al hough he a o emen ioned a icles p o ide heo e ical pe spec i es on biclus-
e ing app oaches, hey ail o add ess he issues posed by biological da ase s and
compu a ional obs acles. Thus, addi ional e alua ions seek o acqui e knowledge by
conduc ing compa a i e analyses o a ious me hodologies. Fo example, E en e al.
[25] ound ha he compu a ional e iciency o biclus e ing app oaches declines
wi h la ge gene exp ession da ase s and mo e biclus e s. Addi ionally, no all me h-
ods can gene a e biologically en iched biclus e s. Padilha e al.’s wo k [26] ei e -
a ed he indings o E en e al. [25] and p o ided u he de ails on he ac o s ha
in luence compu a ional e iciency, including noise in gene exp ession da a, biclus e
o e lap, size, and quan i y. Also, K. Nicholls e al. [27] compa ed a ious biclus e -
ing echniques, no ing ha adap i e algo i hms demons a e supe io compu a ional
pe o mance. They also obse ed a co ela ion be ween noise, da ase size, biclus e
coun , and dec eased compu a ional e iciency. Mo e ecen ly, Cas anho e al. [28]
ha e o e ed ano he s udy in which hey in oduce a uni ied axonomy o biclus e -
ing concep s and de ail a comp ehensi e analysis p ocess. Al hough hey iden i y
h ee main implemen a ion s a egies o big da a biclus e ing algo i hms—pa allel
compu ing, GPU-based execu ion, and MapReduce— hey limi he discussion o
whe he each me hod is a no el p oposal o an adap a ion o exis ing algo i hms.
Based on he highligh s om he a o emen ioned e iews, i can be de e mined
ha biclus e ing e o s ha e long been ying o add ess he limi a ions and imp o e
he quali y o he esul s. Howe e , e ining me a-heu is ics and add essing noise/
Biclus e ing inbioin o ma ics using big da a andHigh… Page 9 o 52 1123
o e lap challenges may no ully ex ac pe inen biological insigh s om as
da ase s. Likewise, cu en biclus e alida ion me hods may lack adequacy when
applied in Big Da a ecosys em. Rega ding hese conce ns, Xie e al. [6] p esen a
compelling s udy on he impac o biclus e ing echniques in cu en biomedical
con ex s. They highligh he lack o biclus e ing algo i hms ailo ed o la ge gene
exp ession da ase s wi h high complexi y. The au ho s ad oca e o he de elopmen
o no el biclus e ing me hods capable o managing biological and biomedical analy-
ses wi hin Big Da a en i onmen s. Also, he e a e o he wo ks ha a e no e iews
bu ha iden i y such p oblems. Fo example, in [29], he au ho s ocus on Big Da a
biclus e ing app oaches using HPC amewo ks like Apache Hadoop and Spa k,
along wi h pa allel sys ems wi h GPUs, o add ess scalabili y challenges. T adi ional
biclus e ing me hods a e deemed imp ac ical o Big Da a asks due o hei inabil-
i y o analyse da a e icien ly. The au ho s ou line key challenges, including le e ag-
ing pa allelism, unde s anding ha dwa e limi a ions, and add essing he inc ease in
biclus e quan i y and size wi h da a olume. They ecommend employing mul iple
alida ion measu es when assessing biclus e ing pe o mance in noisy en i onmen s
o wi h simila biclus e s. Addi ionally, hey cau ion agains o e -in e p e ing s a-
is ical signi icance in gene en ichmen and pa hway analysis, ad oca ing o addi-
ional il e ing o compa ison in Big Da a biclus e ing app oaches.
As he analysis o cu en li e a u e e iews on biclus e ing echniques shows,
many p oposals examine hese echniques om a ious pe spec i es. Howe e , none
o hese wo ks add esses he challenges posed by he big da a en i onmen , such as
p ocessing eno mous amoun s o da a and e alua ing a la ge numbe o biclus e s.
This a icle aims o add ess his gap by p o iding a comp ehensi e s udy o biclus-
e ing echniques om he pe spec i e o high-pe o mance compu ing (HPC).
3.1 C i ical analysis o p e ious wo k andiden i ied esea ch gaps
Al hough a signi ican numbe o challenges ha e con ibu ed o he de elopmen
o biclus e ing me hods o bioin o ma ics and Big Da a en i onmen s, se e al
limi a ions and esea ch gaps pe sis . Fi s , many adi ional biclus e ing algo i hms
exhibi limi ed scalabili y when applied o la ge-scale omics da ase s. These me h-
ods a e o en implemen ed in sequen ial en i onmen s and lack e icien adap a-
ions o dis ibu ed o pa allel compu ing amewo ks. Second, he in e p e abili y
o he esul ing biclus e s is equen ly o e looked, which es ic s hei usabili y in
biomedical con ex s whe e he explainabili y o compu a ional esul s is c ucial o
domain expe s. Ano he ecu en issue is he absence o s anda dised e alua ion
me hodologies; he di e si y o me ics and alida ion s a egies employed ac oss
s udies hampe s ai compa isons and ep oducibili y.
Mo eo e , he e is a gene al lack o in eg a ion o p io biological knowledge
in o he biclus e ing p ocess. Gene on ology e ms, known pa hways, and egula-
o y in e ac ions a e o en igno ed, despi e hei po en ial o guide mo e biologically
meaning ul clus e ing. Fu he mo e, while mode n machine lea ning pa adigms
such as deep lea ning and ensemble lea ning ha e shown success in o he bioin-
o ma ic asks, hei applica ion in biclus e ing emains limi ed. The ep oducibili y
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exp ession ma ix in o chunks co esponding o a ailable p ocesses. Each p o-
cess is esponsible o cons uc ing local biclus e s om i s alloca ed da a chunk,
while he main p ocess in eg a es hese biclus e s o p oduce he inal ou pu . As
da a ansmission among p ocesses is a oided du ing local biclus e cons uc ion,
he au ho s ind he communica ion cos o be negligible. Howe e , hey no e ha
he me hod’s bo leneck a ises om cons uc ing biclus e s wi h a single p ima y
p ocess, which can signi ican ly a ec compu ing pe o mance, especially wi h
excep ionally la ge da ase s. To alida e hei app oach, he au ho s employ a
syn he ic da ase measu ing 12651 x 30 o gene a e 250 biclus e s and claim simi-
la esul s o a yeas gene exp ession da ase [76], hough no suppo ing da a is
p o ided.
Liu e al. [77] in oduce P-Biclus e , a biclus e ing me hod designed o cons uc -
ing shi ing biclus e s [78] om gene exp ession da ase s con aining bo h con inu-
ous and disc e e alues. The echnique ga he s shi pa e ns in o a 2x2 ma ix and
u ilises he de ia ion om each pa e n o de e mine he shi end. Biclus e s a e
hen gene a ed by inc emen ally adding ows and columns o each pa e n un il hei
o se alue alls below a p ede ined h eshold. The au ho s e alua e hei app oach
using eal gene exp ession a ays [79] wi hou noise con ol and compa e esul s
wi h o he sequen ial me hods, assessing biclus e quali y based on de ia ion. How-
e e , alida ion lacks suppo om echniques ensu ing he biological signi icance
o biclus e s. In e ms o compu a ional p ope ies, P-Biclus e employs MPI o
pa allel compu ing on CPU clus e s. The au ho s obse e imp o ed compu a ional
pe o mance wi h se e al p ocesses up o se en bu no e ha pe o mance declines
beyond his h eshold due o a lack o con ol o e dis ibu ed memo y a chi ec u e
and p ocess communica ions. Hence, he me hod is ine ec i e o con inuous da a
ans e o la ge da a olumes. Addi ionally, he s udy lacks de ails on pa allelised
asks, CPU clus e u ilisa ion, synch onisa ion, and da a dis ibu ion, among o he
aspec s.
A. Nisa e al. [80] p esen a scalable and dis ibu ed algo i hm de i ed om
Ahmad e  al.’s wo k [81], which e o mula es he Bipa i e Spec al Pa i ion-
ing echnique [82] using g aph acing o achie e op imal solu ions. Thei me hod
in ol es posi ioning each node o he bipa i e g aph a he ba ycen e o i s neigh-
bou s o iden i y op imal solu ions. The biclus e ing p ocess consis s o wo pa allel
asks: c osso e minimisa ion and biclus e iden i ica ion. Ini ially, he gene exp es-
sion da ase is ho izon ally spli in o homogeneous chunks, each assigned o an
a ailable p ocess. Da a no malisa ion is pe o med, ollowed by i e a i e ow (local)
and column (global) eo de ing. Each p ocess conduc s a local sea ch o biclus-
e s, and ep esen a i e biclus e s a e globally dis ibu ed. The au ho s employ MPI/
C++ o accele a e compu a ional pe o mance and analyse algo i hmic complex-
i y and memo y a chi ec u e managemen . They no e ha MPI-based biclus e ing
algo i hms incu communica ion cos s as da ase size g ows bu claim independence
om ow coun by using c osso e minimisa ion. They demons a e scalabili y by
analysing syn he ic da ase s wi h up o 20 million ows and 64 ixed columns and
using up o 256 p ocesso s. Howe e , he s udy lacks analysis o eal gene exp es-
sion da ase s and compa ison wi h o he me hods o compu a ional accele a ion
echniques.

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MFCM [83] is a biclus e ing echnique de i ed om he C-Means uzzy clus-
e ing algo i hm [84] o gene a ing biclus e s om gene exp ession da ase s. The
me hod u ilises Ma labMPI and an SPMD pa allel compu ing model o dis ibu e
compu a ional wo kload ac oss mul iple p ocesso s. The da ase is di ided in o
chunks co esponding o MATLAB p ocesses, ollowed by gene and sample clus e -
ing o cons uc biclus e s. Gene cen es a e de e mined based on en opy a he han
co ela ion coe icien s, wi h i e a ions con inuing un il a h eshold o ejec ed genes
o a maximum i e a ion limi is eached. Real gene exp ession da ase s a e used,
compa ing esul s o a sequen ial e sion in MATLAB. Compu a ional pe o mance
imp o es a all s eps compa ed o he sequen ial e sion, bu challenges may a ise
wi h inc eased p ocesses o la ge da ase s due o communica ion delays. The s udy
lacks conside a ion o challenges in da a dis ibu ion, memo y a chi ec u e manage-
men , and da a ans e s ac oss p ocesses, and i only e alua es up o eigh p ocesses,
lea ing pe o mance unce ain y wi h mo e.
The Bioconduc o unibic package [85] adap s he UniBic biclus e ing echnique
[86] o pa allel compu ing o gene a e meaning ul biclus e s om gene exp ession
da a. The modi ica ion aims o accommoda e high- h oughpu RNA-Seq, scRNA-
Seq, and PCR da ase s, imp o ing un imes o la ge da ase s wi h hund eds o
housands o columns and housands o ows. The au ho s p o ide de ailed compu-
a ional decisions o pa allelising asks, excep o ma ix disc e isa ion. They e-
implemen he algo i hm in C++11 wi h he OpenMP s anda d o enhanced pe -
o mance. Res uc u ing he sou ce code op imises ea u es and add esses memo y
segmen a ion issues. OpenMP is u ilised o asks like compu ing he Longes Com-
mon Sequence (LCS) [87] be ween ow pai s, dis ibu ed ac oss CPU co es. How-
e e , e idence on he algo i hm’s pe o mance wi h huge da ase s and he a ionale
behind OpenMP usage is lacking. Consequen ly, he scalabili y and pe o mance o
he OpenMP implemen a ion depend on single-machine ha dwa e.
González-Dominguez e  al. [88] de eloped Pa BiBi , a pa allel e sion o he
BiBi algo i hm [89], designed o ex ac biclus e s om bina y da ase s. Pa BiBi ,
implemen ed in C++11, uses MPI and POSIX h eads o exploi bo h sha ed and
dis ibu ed memo y in a hyb id clus e en i onmen . Tasks wi h high compu a ional
cos s, such as pa e n cons uc ion and ow agg ega ion, a e iden i ied. MPI and
POSIX h eads dis ibu e he wo kload ac oss CPUs and nodes o enhance compu-
a ional pe o mance. Howe e , da a ans e s be ween p ocesses may sa u a e he
ne wo k, impac ing pe o mance. López e  al. [90] demons a ed Pa BiBi ’s deg-
ada ion in pe o mance wi h dense da ase s and iden i ied memo y managemen
conce ns o la ge da ase s. F aguela e al. [91] add essed hese issues wi h ScalaPa-
BiBi , also using C++11, MPI, and POSIX Th eads. Sequen ial op imisa ions and
me hodological weaks we e applied o enhance compu a ional speeds and suppo
la ge da ase s. The main di e ence lies in ScalaPa BiBi ’s app oach o gene a ing
pa e ns; i aims o dis ibu e pa e n se s o op imise biclus e elabo a ion. Howe e ,
noise sensi i i y in da ase s emains unadd essed ac oss all e sions.
The COBRAC Con ex biclus e ing algo i hm [92] is capable o iden i ying
po en ial biclus e s along wi h he associa ed ow and column clus e ing dend o-
g ams. This algo i hm ope a es by educing he inpu da ase size and sol ing he
con ex weigh ed biclus e ing p oblem. The u ilisa ion o con ex clus e ing ees
A.López-Fe nández e al.
1123 Page 18 o 52
ensu es obus ness agains mino inpu a ia ions [93]. COBRAC is implemen ed
in C/C++ o he algo i hm i sel and Py hon con aine s o b oade accessibili y.
OpenMP is employed o accele a e he weigh ed con ex biclus e ing ask. While
educing he inpu da ase size imp o es execu ion ime and enables handling la ge
biological da ase s, u he compu a ional documen a ion is needed o elucida e hei
esul s and esou ce usage. Addi ionally, he a ionale behind choosing OpenMP
should be cla i ied, especially ega ding i s dependence on single-machine ha d-
wa e. Al hough he au ho s p io i ise imp o ing usabili y, unning simula ions on
a machine wi h 512 GB o RAM may no e lec ypical compu ing en i onmen s.
Fu he explo a ion wi h la ge da ase s is wa an ed o e alua e he algo i hm’s
scalabili y and pe o mance impac .
The ARBic algo i hm de eloped by Ma e al. [94] is an accele a ed ule-based
biclus e ing algo i hm designed o iden i y ele an gene condi ion pa e ns in bio-
medical da ase s. Implemen ed in C++, ARBic employs OpenMP o pa allelise
he expansion phase o seed biclus e s, which is compu a ionally in ensi e due o
he high dimensionali y o omics da a. OpenMP enables ARBic o exploi sha ed
memo y pa allelism by dis ibu ing he p ocessing o candida e seeds ac oss mul-
iple h eads. This pa allel s a egy signi ican ly educes execu ion ime du ing he
explo a ion o he biclus e sea ch space. Howe e , he use o OpenMP con ines he
algo i hm o sha ed memo y a chi ec u es, limi ing scalabili y o la ge da ase s ha
exceed he memo y capaci y o a single node. Fu he mo e, he inse ion o esul s
in o sha ed s uc u es equi es c i ical sec ions, which may in oduce synch onisa-
ion o e head and limi pa allel e iciency. To enhance ARBic’s pe o mance, u u e
e sions could adop hyb id pa allelism by in eg a ing OpenMP wi h MPI o ask-
based un ime sys ems, allowing dis ibu ed memo y suppo and mo e e ec i e
wo kload balancing. Addi ionally, op imising memo y access pa e ns and educing
con en ion in sha ed da a s uc u es could u he imp o e scalabili y and pa allel
h oughpu .
B. Ba uah e al. [95] in oduce EnsemBic, an assembly-based biclus e ing me hod
ha in eg a es se e al me hods (Laplace P io , iBBiG, and xMo i ) o ex ac unc-
ionally ele an biclus e s om gene exp ession da a. The me hod iden i ies high-
quali y biclus e s using p- alues de i ed om FuncAssocia e 3.0 and employs an
i e a i e app oach o elimina e gene ic simila i ies wi hin biclus e s. The au ho s
emphasise ha he design acili a es he execu ion o base algo i hms in pa allel,
po en ially dec easing compu a ion ime; howe e , he R implemen a ion does no
explici ly inco po a e pa allelisa ion echniques such as OpenMP, MPI, o na i e R
lib a ies o pa allel compu ing. This seeming lexibili y indica es ha pa allelisa-
ion is ei he ex e nally managed o subjec o use implemen a ion. EnsemBic has
been assessed using ou au hen ic da a se s ( wo mic oa ays and wo RNA-seq),
demons a ing consis en enhancemen s o e s andalone me hods in bo h opologi-
cal me ics (in e nal densi y, modula i y, ODF) and biological me ics (GO en ich-
men , pa hway analysis, and alida ion by ChIP-seq). Mo eo e , he inclusion o he
p- alue as a quali y c i e ion ende s he app oach sensi i e o non-signi ican pa -
e ns and enhances i s obus ness agains noise. The algo i hm unc ions on eal con-
inuous da a; howe e , i s use wi h syn he ic, discon inuous, o bina y da a has no
been documen ed.
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The e a e addi ional biclus e ing echniques ha a e accele a ed using con en-
ional pa allel models, bu insu icien in o ma ion is a ailable due o he una ail-
abili y o so wa e o a lack o desc ip ion ega ding he pa allel model and com-
pu a ional esou ces. One such example is PBD-SPEA2 [96], an adap a ion o
he SPEA2 mul i-objec i e e olu iona y algo i hm [97] o gene exp ession da a.
PBD-SPEA2 uses a ixed-leng h, in ege -based encoding o e alua ing biclus e
se s. Objec i e unc ions include MSR [14], BSize [22], and VAR [98], wi h he
Cheng–Chu ch [14] heu is ic o p oducing new solu ions. Pa allel compu ing is
applied o he dynamic coding scheme ask, al hough speci ic pa allelisa ion me h-
ods, s anda ds, o models a e no p o ided, no is he algo i hm’s sou ce code a ail-
able, hinde ing u he explo a ion o compu a ional ea u es.
In MPI-based biclus e ing algo i hms, he size and di e si y o inpu da ase s play
a c ucial ole in speeding up compu ing pe o mance. La ge da ase s o en lead o
memo y managemen and da a ans e challenges due o communica ion sa u a ion
among memo y egions ac oss p ocesso s in he clus e . ScalaPa BiBi o e s a solu-
ion o hese limi a ions. On he o he hand, biclus e ing algo i hms using OpenMP
ace limi a ions inhe en in his implemen a ion, which p ima ily pa allelises p o-
g ams in a sha ed memo y en i onmen , a oiding communica ion o e head bu
es ic ing hem o a single compu e ’s ha dwa e esou ces. The COBRAC algo i hm
exempli ies hese cons ain s by comp essing da ase size o educe execu ion ime
compa ed o i s sequen ial e sion. Addi ionally, conside a ions such as ask selec-
ion o pa allelisa ion, a chi ec u e, and memo y managemen mus be add essed.
While algo i hms may ace limi a ions due o ha dwa e esou ces, such an app oach
ensu es scalabili y wi hou o e loading o memo y o e low. Fu he mo e, as da a-
se olume inc eases, he likelihood o ob aining mo e biclus e s ises, equi ing
ca e ul conside a ion du ing p ocessing and memo y managemen .
5.2 Pa allel biclus e ing wi hbig da a p og amming pa adigms
The eme gence o Big Da a p og amming pa adigms add esses he limi a ions o
adi ional pa allel and dis ibu ed models in handling la ge da ase s e icien ly. Con-
sequen ly, p og amming pa adigms like MapReduce, along wi h pla o ms such as
Apache Hadoop, Apache Spa k, and MATLAB, o e solu ions o massi ely pa -
allel o dis ibu ed biclus e ing. Two ables p esen he cha ac e is ics o he ana-
lysed biclus e ing me hods o you e e ence. Table6 o Appendix A ou lines he
cha ac e is ics o biclus e ing echniques ailo ed o a Big Da a en i onmen . The
i s column lis s he names o he biclus e ing algo i hm o au ho s, i no speci ied.
The second column indica es he Big Da a pla o m used. The ypes o da ase s used
in expe imen s a e de ailed in he hi d column: 0 o syn he ic da ase s, 1 o eal
da ase s, and 2 o a combina ion o bo h. The ou h column deno es whe he com-
pa isons we e made wi h o he s udies: 0 o no compa ison, 1 o compa isons wi h
sequen ial me hods, and 2 o compa isons wi h o he Big Da a-adjus ed biclus e -
ing algo i hms o deploymen on di e en Big Da a pla o ms. The i h column
no es he algo i hm’s abili y o handle noise. Da a ypes used in expe imen s a e
A.López-Fe nández e al.
1123 Page 20 o 52
lis ed in he six h column, acknowledging ha addi ional da a ypes migh also be
compa ible.
Table7 o Appendix A delinea es he compu a ional cha ac e is ics o he dis-
cussed biclus e ing me hods. The i s column p esen s he algo i hm’s name o
au ho s’ names. The second column indica es he numbe o MR jobs equi ed o
gene a ing esul s, whe e 0 signi ies a small numbe and 1 indica es a highe num-
be . The hi d column deno es he me hod’s capabili y o da a spli ing, c ucial o
balancing wo kload and da a ans e s [99]. The ou h column indica es whe he
me hods ha e s a egies o minimise I/O ope a ions, which is c i ical o pe o -
mance, especially in Apache Hadoop se ups. The i h column speci ies i in e medi-
a e da a is s o ed in main memo y o in-memo y compu a ion, educing disk I/O.
The six h column iden i ies whe he MR jobs a e asynch onous, impac ing ha dwa e
and so wa e esou ce u ilisa ion. The se en h column e alua es he in es iga ion o
op imisa ion s a egies, which a e c ucial o e icien pa allelism. The inal column
indica es he ype o scalabili y expe imen s conduc ed: 1 o inc easing da a ol-
ume, 2 o aising he numbe o clus e nodes, and 3 i bo h scalabili y es s we e
pe o med.
BiTM-MR [100] is an ea ly biclus e ing app oach using he MapReduce pa a-
digm, implemen ed wi h Apache Spa k o ex ac ing biclus e s om massi e da a-
se s. The me hod ans o ms da a ma ices in o a block s uc u e o ganised as a opo-
logical map and employs wo bina y ma ices o alida e ow–column ela ionships.
O e coming challenges like da a loading, ailu e sa e y, and algo i hm design, he
au ho s use Apache Spa k o aul co ec ion, da a dis ibu ion, and managemen .
The me hod uses wo MR jobs o i e a e ows and columns and adjus pa ame e s.
The au ho s signi ican ly imp o e compu a ional e iciency by alloca ing independ-
en MR jobs o ows and columns, minimising I/O ope a ions, and enabling asyn-
ch onous p ocessing. Howe e , de eloping a da a pa i ioning echnique such ha
each MR job akes ca e o a g oup o homogeneous ows and columns is an addi-
ional compu ing ac o ha he au ho s ha e no accoun ed o and ha could be
use ul o enhancing hei algo i hm design. Syn he ic da ase s wi h up o wo mil-
lion ows demons a e nea -ideal compu a ional e iciency as da ase size and clus e
co es inc ease. Howe e , compa isons wi h o he biclus e ing algo i hms o pe o -
mance accele a ion esou ces would ha e added alue. The au ho s no e ha Apache
Spa k ini ialisa ion impac s pe o mance mo e on smalle da ase s.
Ruiqi e al. [101] adap ed he non-nega i e ma ix ac o isa ion biclus e ing algo-
i hm [102] using Apache Hadoop o handle 2D spa se ma ices up o one million
by one million. Thei app oach equi es a non-nega i e ma ix and a epe i ion coun
as inpu , using i e MR jobs pe i e a ion o upda e each ou pu subma ix. Howe e ,
his app oach’s hea y eliance on MR jobs may impac compu a ional pe o mance
on la ge da ase s due o equen disk access cos s. Conside a ions such as educ-
ing I/O ope a ions, op imising da a pa i ioning, and enabling asynch onous com-
munica ion be ween map and educe unc ions we e no add essed. The s udy sug-
ges s oom o signi ican imp o emen in bo h compu a ional cos and scalabili y.
An e alua ion wi h syn he ic da ase s and a da ase exceeding one million ows and
columns om he STRING da abase [103] was conduc ed, analysing compu a ional
e iciency conce ning he numbe o nonze o in e ac ions. Howe e , he e was no
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analysis o how well i scales wi h mo e Hadoop clus e nodes o how i compa es o
o he biclus e ing me hods o di e en esou ces o handling la ge da ase s.
MR-GABiT [104] is designed o gene ic biclus e ing o ime se ies da ase s om
mic oa ay expe imen s. Despi e he da ase s being h ee-dimensional, he au ho s
apply biclus e ing o ex ac local pa e ns a di e en ime poin s, which a e hen
compa ed o de e mine op imal pa e ns. Using he MATLAB MapReduce ame-
wo k, hey add ess dimensionali y and scalabili y issues. Da a pa i ioning by ime
poin s educes MR job coun , imp o ing compu a ional pe o mance. Each MR ask
employs a map unc ion o ex ac he bes indi idual om da a chunks, wi h educe
unc ions combining hese bes indi iduals. Howe e , limi a ions include he inabil-
i y o educe disk I/O ope a ions and educe unc ions wai ing o all map unc ions
o comple e. E alua ion wi h yeas cell cycle ime se ies da ase s [105] is conduc ed,
bu he scalabili y wi h inc eased clus e nodes is no es ed, and he e is no com-
pa ison wi h o he gene ic biclus e ing me hods o Big Da a esou ces.
The e a e addi ional biclus e ing s udies using he MapReduce pa adigm whe e
compu a ional de ails a e insu icien and he so wa e is una ailable. One such wo k
is MCC [106], an adap a ion o he Cheng–Chu ch (CC) biclus e ing echnique [14].
MCC andomly gene a es nume ous unique suba ays and execu es mo e compu a-
ions on hem han he o iginal CC algo i hm, making i compu a ionally in ensi e.
While MapReduce is used, speci ics on i s adap a ion, da a pa i ioning, and dis i-
bu ion a e no p o ided. Real gene exp ession da ase s show ha MCC wo ks be -
e han he sequen ial CC algo i hm in e ms o accu acy and speed. The au ho s
acknowledge MCC’s ine iciency in memo y managemen due o he need o s o e
nume ous in e media e a ays and he high numbe o I/O ope a ions.
As can be seen, his subsec ion highligh s he sca ci y o bioin o ma ics- ela ed
biclus e ing algo i hms on Big Da a pla o ms like Spa k o Hadoop. Consequen ly,
his subsec ion also includes biclus e ing algo i hms ha ha e been c ea ed in disci-
plines o science un ela ed o bioin o ma ics bu ha a e based on he MapReduce
pa adigm. Then, i is possible o c ea e ais as o whe he he lack o algo i hms is
due o he complexi y o he biclus e ing echnique’s de ini ion o o scien i ic ield-
speci ic ac o s in bioin o ma ics. DisCo [107] ea anges subma ix ows and col-
umns o mee a quali y h eshold, using key– alue pai s o s o e ows. Bha naga
e al. [108] de eloped a MapReduce biclus e ing echnique using o mal concep
analysis (FCA) o bina y da ase s [109]. Thei ocus is on imp o ing compu a ional
pe o mance as da ase size inc eases, al hough hey ha e no explo ed scalabili y
wi h node numbe s, and he BiBi sequen ial biclus e ing me hod achie es as e
execu ion speeds in eigh o he ou een o e all pe o mance e alua ions. Lin e al.
[4] de eloped biclus e ing algo i hms o he elecommunica ions ield, wi h e -
sions o Spa k (SP-PLSS) and Hadoop (MR-PLSS). They aim o iden i y p o i able
cus ome s wi h simila buying beha iou s, esul ing in be e e iciency and scalabil-
i y wi h Apache Spa k o la ge da ase s. This analysis demons a es ha , ega dless
o he ield o s udy, biclus e ing echniques ha e no been widely implemen ed on
Big Da a pla o ms such as Apache Spa k and Apache Hadoop. In addi ion, he e is
a signi ican gap be ween he p esen numbe o biclus e ing algo i hms p oduced on
hese Big Da a pla o ms and o he ypes o compu a ional accele a ion applica ions.
As a esul , mul iple hypo heses may eme ge om di e se iewpoin s.

A.López-Fe nández e al.
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Many adi ional biclus e ing me hods need o cons an ly p ocess one pa o
he da ase , which makes i di icul o design hese me hods [14, 89, 94, 110, 111].
Consequen ly, no all s a egies easily adap o he MapReduce pa adigm due o his
complexi y. The algo i hm design mus conside ac o s like MR job coun , da a-
se pa i ioning, minimising I/O ope a ions, in-memo y sys ems, and asynch onous
communica ion be ween map and educe unc ions. P ocessing la ge da ase s can
g ea ly enhance pe o mance, bu he di e ences in bioin o ma ics da ase s migh
make i ha d o hese algo i hms o wo k well, especially when he e a e only a ew
genes and expe imen al condi ions [108]. Also, Big Da a pla o m ini ialisa ion ime
impac s pe o mance [49, 100], and issues like educe unc ions wai ing o map
unc ion comple ion and uncon olled execu ion o de can deg ade pe o mance.
Biclus e ing algo i hms on Apache Spa k ou pe o m hose on Apache Hadoop due
o in e media e I/O ope a ions elying on memo y a he han disk [4].
5.3 Pa allel biclus e ing onGPUs
The GPGPU concep ha nesses he p ocessing esou ces o GPU de ices o accel-
e a e compu a ional pe o mance, making hem iable al e na i es o supplemen s
o CPU p ocessing o biclus e ing algo i hms [112]. Howe e , biclus e ing algo-
i hms ailo ed o GPGPU mus conside a ious ac o s ou lined in subsec ion4.3
o op imise compu a ional e iciency and suppo Big Da a da ase s. These ac o s
a e analysed o each biclus e ing algo i hm adap ed o GPGPU and p esen ed in
wo ables. Table8 o Appendix A p o ides key ea u es o biclus e ing echniques
implemen ed on GPGPU. The i s column lis s algo i hm names, while he second
column indica es da ase ypes: 0 o syn he ic, 1 o eal, and 2 o bo h. The hi d
column indica es i compa isons we e made wi h o he me hods: 1 o sequen ial
me hods and 2 o o he GPGPU-adap ed algo i hms. The ou h column no es i
he app oach handles da ase noise. The i h column speci ies he da a ypes used,
acknowledging ha o he ypes may also be applicable. The inal columns de ail
suppo ed da a ypes o each algo i hm.
Table9 o Appendix A de ails he compu a ional aspec s o biclus e ing algo-
i hms. The i s column lis s algo i hm names. The second column speci ies mul i-
GPU suppo and he pa allelism echnique. Sha ed memo y usage is no ed in he
hi d column. The ou h column indica es he use o coalescence. The memo y allo-
ca ion me hod is speci ied in he i h column (0 o non-pinned, o he wise pinned).
The da a ans e me hod (global memo y o uni ied memo y) is desc ibed in he
six h column. Occupancy le els, e lec ing esou ce u ilisa ion, a e in he se en h
column. To be e e lec he compu a ional oo p in o he su eyed ools, we clas-
si ied esou ce occupancy in o h ee ca ego ies based on es ima ed a e age usage o
compu a ional esou ces: low (<40%), medium (40–70%), and high (>70%). The
inal column indica es how synch onisa ion poin s a e used.
A nedo-Fe nández e al. [113] in oduced an adap a ion o he FLOC biclus e -
ing me hod [114], ocusing on he compu a ional in ensi y o calcula ing he MSR
measu e [14] o biclus e alida ion. U ilising CUDA o his ask, hey demons a e
imp o ed compu a ional pe o mance compa ed o he sequen ial e sion, using up
Biclus e ing inbioin o ma ics using big da a andHigh… Page 23 o 52 1123
o 2000 x 2000 squa e syn he ic da ase s. Howe e , hey did no ully exploi he
GPU’s capabili ies, no ing da a ans e ia he PCI-Exp ess bus as a bo leneck
and no conside ing mul i-GPU wo kload spli ing. The au ho s also discussed he
impac o h ead coun pe CUDA block on compu a ional e iciency, obse ing
diminishing e u ns wi h excessi ely high h ead coun s. Rega ding memo y man-
agemen , he au ho s do no speci y he a ious o ms o memo y p esen in a GPU
de ice. The e o e, i is assumed ha da a is always used om he GPU’s global
memo y.
Liu e al. [17] pa allelised he ask o p ocessing column pai s in he Geome -
ic Biclus e ing algo i hm (GBC) [115]. They de eloped h ee pa allel implemen-
a ions: one using POSIX Th eads, ano he using CUDA, and a hi d employing a
ield-p og ammable ga e a ay (FPGA) [116]. In he CUDA e sion, columns a e
pai ed pe CUDA block, wi h each h ead esponsible o combining a po ion o
each column using sha ed memo y o ensu e high coalescence and a oid memo y
con lic s. Real da ase expe imen s indica e ha he GPU e sion achie es he high-
es speedup, while he FPGA e sion is he mos ene gy-e icien . The au ho s dis-
co e ha minimising da a ans e s be ween RAM and GPU global memo y is c u-
cial, especially o la ge da ase s in he GPU e sion. In ano he wo k, Liu e al.
[117] apply he GBC algo i hm o iden i y neu al p ocessing pa e ns in mic oa ay
da ase s, p esen ing h ee CUDA e sions wi h dis inc op imisa ions. The i s e -
sion uses sha ed memo y o s o e column pai chunks, ensu ing no o e head ega d-
less o chunk size. A e loading he a ge column pai in o sha ed memo y, me ging
ope a ions a e conduc ed, and ou comes a e s o ed in global memo y. Mo eo e , he
chunking o column pai s p e en s he sha ed memo y om o e loading, necessi a -
ing a global synch onisa ion o e e y p ocessed chunk. Acco ding o he au ho s,
his global synch onisa ion incu s a cos by es o ing ine icien column pai s in he
global memo y. The second e sion a emp s ope a ions on mul iple column com-
bina ions o mi iga e his, while he hi d e sion ocuses on educing index upda e
ime. In o de o accomplish his, hey examine he e ec o con inuous ans e s
be ween GPU de ices and he CPU, a p oblem iden i ied in p io wo k [17]. Real
da ase expe imen s show he second e sion achie es he bes speedup, emphasis-
ing he impo ance o da a euse, minimising global memo y accesses, and dis ib-
u ing wo kloads be ween CPU and GPU o la ge da ase s. Despi e hese op imisa-
ions, none o hese e sions implemen s a mul i-GPU design, which could u he
enhance wo kload alloca ion and suppo o la ge da ase s.
Mejía-Roa e al. [118] in oduced a mul i-GPU adap a ion o he non-nega i e
ma ix ac o isa ion (NMF) [102] algo i hm, using he MPI s anda d o mul i-GPU
synch onisa ion. The algo i hm pa i ions he da ase in o blocks and dis ibu es i
ac oss GPUs, subsequen ly decomposing he da a in global memo y. A 1D block
se up achie es coalescence by accessing consecu i e memo y add esses. Asynch o-
nous CPU- o-GPU da a ans e s enhance compu a ional pe o mance. Real da ase
expe imen s con i m ha he GPU e sion ou pe o ms he sequen ial CPU-based
algo i hm, wi h speedup inc easing wi h da ase size. Scalabili y analysis e eals
ha ac o s like da a ans e s, synch onisa ion o e heads, and da ase size a e c i i-
cal in mul i-GPU designs [119]. Fo la ge da ase s, he mul i-GPU app oach is p e -
e able, while a single GPU su ices o smalle ones.
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1123 Page 24 o 52
The Condi ion-dependen Co ela ion Subg oups (CCS) [120] algo i hm, de el-
oped in CUDA, ully u ilises GPU de ices o biclus e gene a ion. Real and syn-
he ic da ase s demons a e a 20x speedup compa ed o he sequen ial implemen-
a ion. Howe e , unlike p e ious wo ks, he algo i hm does no employ da ase
chunking using CUDA block/ h ead decomposi ion, which limi s i s scalabili y o
he GPU memo y size. Sha ed memo y expedi es biclus e c ea ion bu may o e -
load wi h excessi e columns, capped a 200. Each CUDA block cons uc s a biclus-
e using a single h ead, esul ing in low occupancy. The me hod lacks suppo o
mul i-GPU a chi ec u es; hus, i does no explo e wo kload dis ibu ion o scalabil-
i y o la ge da ase s.
EBIC, de eloped by O zechowski e al. [121], is a pa allel e olu iona y biclus e -
ing me hod designed o disco e nume ous ele an pa e ns wi h high p ecision.
I uses a mul i-GPU a chi ec u e o dis ibu e da ase ows ac oss GPUs, handling
he en i e biclus e gene a ion p ocess. The au ho s op imise wo kload dis ibu ion
ac oss GPUs, CUDA blocks, and h eads by using sha ed memo y o compu ing
i ness unc ions and gene a ing biclus e s. Expe imen al esul s wi h syn he ic and
eal da ase s demons a e EBIC’s compu a ional e iciency, high coalescence, and
occupancy. EBIC ou pe o ms al e na i e echniques like CCS by up o 12 imes o
la ge da ase s. Howe e , he algo i hm’s scalabili y o Big Da a is limi ed by he
maximum suppo o 60000 ows pe GPU de ice.
González-Domínguez e al. [122] implemen ed CUBiBi , a mul i-GPU e sion
o he BiBi [89] biclus e ing algo i hm. CUBiBi employs GPU de ices o add
ows o po en ial biclus e s, wi h he emaining p ocessing done on he CPU. In he
case o a mul i-co e p ocesso , i can exploi he mul iple CPU co es while inding
po en ial biclus e s hanks o a pa allelisa ion wi h POSIX h eads. CuBiBi employs
he GPU’s sha ed memo y o s o e seeds o compu a ional pe o mance. Howe e ,
his decision limi s scalabili y o la ge da ase s because his memo y is iny and he
numbe o pa e ns inc eases as he da ase g ows. Fu he mo e, he o iginal me hod
ou pe o ms i o small da ase s. gBiBi [90], ano he e sion o BiBi , uses a mul i-
GPU a chi ec u e o p ocess massi e bina y da ase s e icien ly. The me hodology
add esses da a ans e , wo kload dis ibu ion, and esou ce u ilisa ion by ou pe -
o ming o he adap a ions and being he only modi ied e sion capable o handling
big da ase s. In ano he wo k, he au ho s c ea ed a Py hon package named bioSci-
ence ha uses HPC o speed up a ious da a mining echniques, such as he BiBi
algo i hm, using CPU and mul i-GPU clus e s [123].
The u ilisa ion o GPU de ices o accele a ing biclus e ing algo i hms has
become a p e alen end, wi h CUDA being he mos used pla o m o his pu -
pose. Va ious aspec s need conside a ion when de eloping and op imising biclus-
e ing algo i hms in a mul i-GPU en i onmen , as obse ed in he p e ious wo ks.
While GPGPU pa allel algo i hms gene ally ou pe o m sequen ial ones in e ms
o compu ing pe o mance, many s uggle o handle massi e da ase s e ec i ely.
In he majo i y o he analysed algo i hms, au ho s s ess he impo ance o plan-
ning esou ce alloca ion o GPU u ilisa ion. CCS, o ins ance, employs a CUDA
block o pa allelise a ask, whe eas CUBiBi , NMF, EBIC, and o he algo i hms use
he h eads o a CUDA block o do he pa allelisa ion ask. This decision is di ec ly
dependen on he algo i hm’s occupancy a e and, consequen ly, on whe he he
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implemen a ion will use he ull powe p o ided by he g id o each GPU de ice.
Fo algo i hms suppo ing mul i-GPU a chi ec u es, wo kload dis ibu ion among
de ices is ca e ully conside ed. These wo ks use pa allelisa ion echniques like
POSIX Th eads, MPI, and OpenMP o dis ibu e asks, bu he mos e ec i e s a -
egy emains unclea due o he a ie y o op ions a ailable.
Rega ding memo y- ela ed elemen s, he au ho s o se e al wo ks iden i y
CPU–GPU da a ans e as a pe o mance bo leneck due o communica ion bus lim-
i a ions, leading some o minimise ans e s. Con inuous global memo y access can
also impede e iciency, p omp ing he use o sha ed memo y o boos speed. How-
e e , imp ope sha ed memo y use, as seen in CuBiBi o CCS, can hinde pe o -
mance on la ge da ase s o smalle da ase s. Thus, sha ed memo y usage’s e icacy
a ies wi h da ase size and should be ca e ully assessed. Mos algo i hms employ
ixed memo y ese a ion and alloca ion s a egies. Synch onisa ion be ween GPU
de ices is c ucial in mul i-GPU sys ems, bu i a ies based on algo i hm echnique.
Fo example, NMF and gBiBi use asynch onous communica ions o minimise syn-
ch onisa ion and imp o e e iciency.
5.4 Pe o mance conside a ions ingene exp ession da a analysis
Implemen ing biclus e ing algo i hms using HPC echnologies in big da a en i on-
men s has shown a iable pe o mance depending on he speci ic cha ac e is ics o
he biological ask. In he con ex o gene exp ession da a analysis, which ypically
in ol es la ge-scale, high-dimensional, and spa se ma ices, each compu a ional
model p esen s dis inc ad an ages and limi a ions. Benchma k da ase s a e com-
monly u ilised o assess he pe o mance and scalabili y o HPC biclus e ing me h-
ods wi hin a Big Da a amewo k, ensu ing bo h e icacy and biological signi icance.
The Cance Genome A las (TCGA) and he Geno ype-Tissue Exp ession (GTEX)
p ojec [124] a e he mos equen ly u ilised esou ces. The TCGA o e s ex ensi e,
high-dimensional gene exp ession da a ac oss many cance ypes, se ing as a alu-
able esou ce o assessing he scalabili y and p ecision o biclus e ing algo i hms
in eal-wo ld biomedical con ex s. Con e sely, GTEX p o ides comp ehensi e
ansc ip ome da a om se e al non-diseased human issues, acili a ing he iden-
i ica ion o issue-speci ic gene co-exp ession modules. The ex ensi e u ilisa ion o
TCGA and GTEX s ems om hei public accessibili y and comp ehensi eness, as
well as he chance hey p o ide o e alua e algo i hmic esilience in high- h oughpu
se ings, which is c ucial o implemen ing biclus e ing app oaches wi hin HPC o
Big Da a amewo ks. Syn he ic da ase s a e u ilised in a ious esea ch o e alua e
algo i hm pe o mance unde con olled se ings o noise, size, and s uc u e.
Table 1 p o ides a compa a i e summa y o he main HPC pa adigms dis-
cussed in his sec ion, ou lining hei associa ed echnologies, s eng hs, limi a-
ions, and ypical applica ions in gene exp ession biclus e ing asks. T adi ional
pa allel and dis ibu ed models, such as MPI-based clus e s, a e p o icien a
managing la ge-scale gene exp ession da ase s, especially in jobs cha ac e ised
by signi ican da a pa allelism, such as gene o sample il e ing. Ne e heless,
hei e icacy may diminish in i e a i e biclus e ing con ex s due o in e -node
A.López-Fe nández e al.
1123 Page 32 o 52
Based on he esea ch conduc ed in he p eceding sec ions o his wo k, he p i-
ma y challenges ha biclus e ing mus o e come in o de o adap o a Big Da a
ecosys em a e ou lined below. These challenges a e ca ego ised in o ou p ima y
g oups: da ase s, biclus e ing app oaches, biclus e ing alida ion app oaches, and
isualisa ion and in e p e a ion o he esul s.
7.1 Da a‑cen ic challenges
Due o he exponen ial g ow h o biological and biomedical da a alongside ad ance-
men s in NGS [166] echnologies, bioin o ma ics is con on ed wi h signi ican
challenges in s o ing and analysing as da ase s. The pace o da a gene a ion om
sequencing ou paces he compu a ional esou ces’ capaci y o p ocess such la ge
olumes, leading o an expanding gap be ween hem [167]. Nume ous p ojec s and
eposi o ies a e eme ging o s o e inc easingly massi e and in ica e da ase s. Fo
ins ance, The Cance Genome A las (TCGA) [18] amassed 2.5 pe aby es o di e se
biological da a, including mRNA, miRNA, and p o ein exp ession da a, along wi h
his ology slides and gene ic a ia ion da a. The ENCODE p ojec [168] ocuses on
anno a ing unc ional sequences in he human genome, expanding o include da a
om o he o ganisms like mice, lies, and wo ms, o alling o e i e e aby es.
The Eu opean Bioin o ma ics Ins i u e (EBI) [169] s o es mo e han 390 pe a-
by es o aw biological da a, making i one o he la ges eposi o ies globally. As
Biclus e ing Pipeline in Big Da a
Bioin o ma ics wi h HPC In eg a ion
Da a Sou cesGenomic Da a,
Gene Exp ession Ma ices
(HDFS/Cloud S o age)
P ep ocessingNo maliza ion,
Fea u e Selec ion(Spa k + GPU) Biclus e ing Algo i hmsDis ibu ed
FLOCK, Plaid(MPI + CUDA)
Visualiza ionHea maps, D3.js,
Tableau
Applica ionsD ug Disco e y,
Mul i-Omics
Big Da a
HPC
DATA SOURCES DATA
PREPROCESING
BICLUSTERING
VISUALIZATION
Valida ion & E alua ionGO
En ichmen , Scalabili y Me ics
VALIDATION
APPLICATIONS
Fig. 3 Bioin o ma ics Big Da a biclus e ing pipeline wi h HPC in eg a ion (MPI/GPU), designed o
scalabili y in applica ions such as d ug disco e y and mul i-omics in eg a ion

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hese eposi o ies g ow in olume, complexi y, and di e si y, ex ac ing meaning ul
insigh s becomes inc easingly challenging.
The c ea ion and p ep ocessing o da ase s om NGS pla o ms can impac da a
p ecision and quali y due o ac o s like low-quali y eads, duplica e eads, o inse -
ions/dele ions [170]. Reads ep esen he sequenced base pai s (bp) om DNA ag-
men s. Da a ans o ma ion, such as om FASTQ o FASTA, is c ucial o e i ying
da a quali y and elimina ing noise [171]. Howe e , he p oduc ion and p ep ocessing
o la ge da ase s a e ime-consuming p ocesses. To add ess hese challenges, he sci-
en i ic communi y is de eloping Big Da a and HPC ools using dis ibu ed memo y
sys ems [172–174].
The con inuous gene a ion o biological and biomedical da a p esen s challenges
in s o age and managemen . Cloud compu ing has eme ged as an e ec i e solu ion,
as e idenced by a ious s udies [175, 176]. Despi e i s bene i s, e o s a e unde -
way o de elop da a comp ession echniques o educe cloud compu ing cos s [177].
La ency is ano he conce n, as da a e ie al om he cloud o scien i ic da a analy-
sis can be ime-consuming. Shi ing da a p ocessing o he cloud can add ess his
issue, educing la ency and cos s and accele a ing esul gene a ion. Amazon AWS,
o example, o e s he Amazon AWS Genomics se ice, equipped wi h ools o
p ocess la ge olumes o genomic da a e icien ly [178, 179]. Cloud-based s o age
equi es conduc ing da a analysis compu a ions in he cloud o mi iga e la ency in
esul gene a ion. Scalable ools like SeqPig [180], BigBWA [181], GMQL [182],
SeQuiLa-co [183], and SeQual [184] ensu e da a in eg i y du ing quali y con ol
and p ep ocessing, especially when dealing wi h ex ensi e aw sequencing da a.
7.2 Algo i hmic challenges
T adi ional biclus e ing me hods ha e p ima ily ocused on imp o ing esul qual-
i y, bu hey o en s uggle wi h handling la ge da ase s, esul ing in educed use ul-
ness. Challenges in p oducing biclus e s om ex ensi e da ase s, in luenced by a i-
ous ac o s such as applica ion domains, ypes, s uc u es, o dimensions, con ibu e
o his issue [6, 146]. Addi ionally, he cha ac e is ics o la ge biological da ase s
inc ease he likelihood o gene a ing la ge and mo e nume ous biclus e s, equi ing
he use o Big Da a and HPC ools o expedi ed biclus e gene a ion.
To choose he app op ia e Big Da a o HPC applica ion o designing a biclus-
e ing me hodology, unde s anding he p oblem’s na u e, pa allelisable asks, and
da ase cha ac e is ics is c ucial. Fo asks equi ing high p ocessing capabili y wi h
la ge - han-usual bu no massi e da ase s, pa allel o dis ibu ed adi ional models
a e ecommended. Da ase size is pi o al, in luencing memo y managemen , da a
ans e , and communica ion la ency, pa icula ly in MPI-based me hods when using
dis ibu ed memo y o capaci y. This issue does no a ec mul i- h eaded me hods
(e.g. OpenMP o POSIX Th eads), which ely on a single compu e ’s ha dwa e
bu p eclude building CPU clus e s o enhanced pe o mance. To op imise mul i-
h eaded algo i hm pe o mance, i is ecommended o limi pa allel ask complexi y
and comp ess da ase s o con ol p ocessing and s o age cos s.
A.López-Fe nández e al.
1123 Page 34 o 52
Pla o ms like Apache Hadoop o Apache Spa k a e ecommended o handling
la ge da ase s equi ing inc eased p ocessing cos s and compu a ional esou ces,
acili a ing he de elopmen o biclus e ing solu ions o such da ase s. Howe e ,
he e exis s a gap be ween he numbe o biclus e ing algo i hms designed o hese
pla o ms and hose o o he applica ions aimed a imp o ing compu a ional pe o -
mance. Adap ing biclus e ing algo i hms o hese pla o ms o cons uc biclus e s
ia da ase pa i ioning poses c i ical challenges such as he numbe o MapReduce
jobs, in elligen da ase pa i ioning s a egies, educed I/O ope a ions, and asyn-
ch onous communica ion be ween map and educe unc ions. Fu he mo e, hese
biclus e ing echniques o en exhibi poo pe o mance on small da ase s due o pla -
o m ini ialisa ion ime, signi ican ly educing algo i hm e icacy. Some compa a-
i e s udies ha e shown ha biclus e ing algo i hms de eloped using Apache Spa k
o e supe io compu a ional pe o mance compa ed o Apache Hadoop, a ibu ed
o educed I/O ope a ion cos s and as e access speeds.
Recen ly, GPU de ices and he CUDA pla o m ha e become popula o accel-
e a ing biclus e ing algo i hms, demons a ing op imal compu a ional pe o mance.
Howe e , no all GPU-based biclus e ing algo i hms can handle huge da ase s e ec-
i ely, which poses a challenge in de eloping algo i hms o such olumes. Maxim-
ising GPU powe u ilisa ion h ough esou ce planning is c ucial, alongside mini-
mising da a ans e s be ween CPU and GPU o mi iga e bandwid h limi a ions.
While sha ed memo y o e s speed, i s use may lead o memo y o e lows in p o-
cessing massi e da a olumes, equi ing ca e ul selec ion o i s usage. Mul i-GPU
a chi ec u es can enhance pe o mance, bu wo kload dis ibu ion and synch onisa-
ion among GPU de ices a e essen ial. This dis ibu ion ypically employs pa allel
o dis ibu ed me hods like OpenMP, POSIX Th eads, o MPI, while asynch onous
communica ion ac oss GPU de ices has been shown o imp o e compu a ional pe -
o mance by omi ing synch onisa ion.
7.3 Challenges in alida ion
Biclus e ing algo i hms o en gene a e a as numbe o esul s o e la ge da ase s,
which equi e subsequen alida ion [17, 29]. Typically, alida ion in ol es s a is i-
cal o biological knowledge-based me hods, o en sou ced om public da abases, o
asce ain he biological ele ance o he gene a ed ou comes [127]. Recen ad ance-
men s in biclus e alida ion me hods and ools ha e p ima ily ocused on imp o -
ing esul accu acy and de eloping use - iendly in e aces. Howe e , he e is a
no able absence o me hodologies and ools capable o acili a ing e icien compa -
a i e analysis o biclus e alida ion app oaches. Fo ins ance, EBIC, a mul i-GPU
e olu iona y biclus e ing me hod, limi ed i s ou comes o 100 and alida ed hem
using he sequen ial so wa e RGOS a s [160], which ope a es solely wi h gene lis s.
The e is a p essing need o de elop alida ion echniques o la ge biclus e
da ase s. These echniques would enable esea che s o alida e biclus e s de i ed
Biclus e ing inbioin o ma ics using big da a andHigh… Page 35 o 52 1123
om da ase s managed by scien i ic ini ia i es o gene a ed om nex -gene a ion
sequencing. Addi ionally, i has been demons a ed ha i is no possible o d aw
in e es ing biological knowledge om he huge numbe o biclus e s gene a ed by
Big Da a-adap ed biclus e ing algo i hms.
7.4 Visualisa ion andin e p e abili y issues
A e biclus e ing esul s a e alida ed, esea che s mus ocus on isualising and
in e p e ing hem in biological and biomedical con ex s o de i e eliable conclu-
sions. Se e al isualisa ion echniques aid in in e p e ing biclus e ing algo i hm
ou comes. BicO e lappe [185] add esses he challenge o isualising biclus e
o e lap by using in e sec ing hulls, enabling he ex ac ion o biological insigh s
h ough in e ac i e isualisa ion. BiVisu [186] employs Pa allel Coo dina e (PC)
plo s and objec i e me ics like MSR [14] and A e age Co ela ion Value (ACV)
[187] o assess biclus e homogenei y. BiDo s [188] explo es new isual and in e ac-
i e me hods o in es iga ing weigh ed biclus e s ac oss domains. O he conside a-
ions in biclus e isualisa ion include he limi a ions o hea maps and PC plo s in
demons a ing biological ele ance [189]. The au ho s ad oca e o no el isualisa-
ion echniques and p opose s anda ds o biclus e ing algo i hm ou pu s o acili a e
isualisa ion ool in eg a ion. Addi ionally, ne wo k-based isualisa ion app oaches
simpli y he in e p e a ion o biclus e isualisa ion ne wo ks [159, 190].
These isualisa ion ools can help assess he cohe ence o biclus e s o med by
biclus e ing echniques, indica ing hei po en ial signi icance. Howe e , i is s ill
possible ha some biclus e s hold domain- ele an knowledge [146]. Ex e nal
sou ces o domain expe e alua ion may be needed o con i m he au hen ici y o
hese biclus e s. Despi e e o s o s anda dise biclus e ing algo i hm ou pu s and
u ilise isualisa ion and en ichmen analysis ools o be e biological in e p e a-
ion, he shee olume o biclus e s gene a ed in a Big Da a se ing can lead o a
loss o unc ionali y. Consequen ly, he e is a need o isualisa ion ools ailo ed o
enhance biological in e p e a ion and manage la ge biclus e da ase s.
8 Conclusions
The g owing olume o biological and biomedical da a necessi a es he adap a ion o
adi ional biclus e ing me hods o e ec i ely manage la ge-scale da ase s. To mee
his challenge, ecen ad ancemen s ha e ocused on employing High Pe o mance
Compu ing (HPC) and Big Da a amewo ks—such as Apache Spa k, GPU accel-
e a ion, and dis ibu ed/pa allel a chi ec u es— o imp o e compu a ional e iciency.
Al hough hese me hodologies o e subs an ial bene i s, hey also in oduce chal-
lenges ela ed o communica ion o e head, memo y managemen , and ini ialisa ion
A.López-Fe nández e al.
1123 Page 36 o 52
la ency, pa icula ly in MPI-based and MapReduce amewo ks. GPU-based solu-
ions, while powe ul, equi e ca e ul esou ce alloca ion, CUDA g id op imisa-
ion, and e icien da a ans e be ween RAM and GPU memo y o achie e peak
pe o mance.
Beyond compu a ional conside a ions, alida ion is essen ial o con i ming
he eliabili y and biological signi icance o biclus e ing ou comes. Con empo-
a y alida ion s a egies—based on s a is ical consis ency o gene en ichmen
analysis—s uggle o scale wi h he as numbe o biclus e s p oduced by mod-
e n algo i hms. Consequen ly, many ools limi hei assessmen s o a subse o
esul s, highligh ing he need o mo e scalable and au oma ed alida ion ech-
niques ha le e age HPC in as uc u e.
This s udy has iden i ied mul iple a enues o imp o emen . Fu u e esea ch
should ocus on de eloping biclus e ing algo i hms ha a e inhe en ly scal-
able and op imised o dis ibu ed and he e ogeneous compu ing en i onmen s.
Imp o ing he in e p e abili y o biclus e ing esul s is also essen ial o acili a e
adop ion by biomedical esea che s, which may in ol e he use o in e p e able
models o isualisa ion ools ha cla i y he biological ele ance o he de ec ed
pa e ns. S anda dised, open benchma king amewo ks ha ely on eal-wo ld
da ase s a e also needed o suppo ep oducible and compa a i e assessmen s.
One p omising app oach in ol es in eg a ing p io biological knowledge—
such as unc ional anno a ions o egula o y ne wo ks—in o he biclus e ing
p ocess o guide he disco e y o biologically meaning ul s uc u es. The ield
can also bene i om he inco po a ion o ad anced machine lea ning me hods,
including deep lea ning, g aph-based models, and eme ging o ms o a i icial
in elligence such as ein o cemen lea ning and ans o me s. These echnolo-
gies a e expec ed o play a cen al ole in he nex gene a ion o biclus e ing
me hods, enabling mo e adap i e, con ex -awa e, and au oma ed disco e y o
complex pa e ns in omics da a.
Expanding he applica ion o biclus e ing o mul i-omics in eg a ion and ime
se ies analysis could p o ide mo e comp ehensi e insigh s in o dynamic bio-
logical sys ems. Fu he mo e, u u e algo i hms should enhance obus ness o
noise h ough he use o obus s a is ical echniques and p ep ocessing s a -
egies. S eng hening collabo a ion wi h expe imen al biologis s is also key o
alida ing compu a ional indings in i o o in i o, he eby suppo ing hei
ansla ional ele ance.
Looking ahead, an icipa ed mains eam ends include he in eg a ion o
biclus e ing in o au oma ed machine lea ning (Au oML) pipelines, enabling
non-expe use s o conduc explo a o y analyses in biomedical con ex s. Addi-
ionally, he deploymen o biclus e ing wo k lows wi hin cloud-based HPC
en i onmen s will enhance scalabili y and accessibili y. Ano he impo an
di ec ion is he explo a ion o ede a ed lea ning amewo ks o enable secu e
Biclus e ing inbioin o ma ics using big da a andHigh… Page 37 o 52 1123
and p i acy-p ese ing biclus e ing ac oss dis ibu ed biomedical da ase s—a
c ucial conside a ion in mul i-ins i u ional and clinical se ings.
By add essing hese limi a ions and oppo uni ies, nex -gene a ion biclus e -
ing echniques can e ol e in o powe ul, in e p e able, and scalable ools o
analysing high-dimensional biological da a. The ising adop ion o deep lea n-
ing and AI-d i en me hodologies in bioin o ma ics ep esen s a p omising di ec-
ion. In es iga ing how such models can be e ec i ely adap ed wi hin HPC and
Big Da a ecosys ems—pa icula ly in alignmen wi h he analy ical s a egies
discussed in his pape —cons i u es a signi ican a ea o u u e esea ch.
Appendix A: De ailed ables
See Tables2, 3, 4, 5, 6, 7, 8 and 9
All he ables ha a e p esen ed below a e e e enced in he main ex .
Table 2 A summa y o he mos signi ican biclus e ing e iews
Yea Au ho s Applica ion Me hods Valida ion
2004 Madei a e al. [19] Gene exp ession X
2005 Tanay e al. [23] Gene exp ession X
2007 San ama ía e al. [24] Gene exp ession X
2008 Busygin e al. [20] Biomedicine and ex mining X
2010 Ras ega e al. [191] Gene exp ession X
2010 Ve ma e al. [192] Gene exp ession X
2013 E en e al. [25] Gene exp ession X
2013 O zechowski. [128] Gene exp ession X
2014 Oghabian e al. [193] Gene exp ession X
2014 Ho a e al. [194] Gene exp ession X
2015 Pon es e al. [195] Gene exp ession X
2015 Pon es e al. [21] Gene exp ession X
2015 Mouni . [196] Gene exp ession X
2016 Biswal e al. [197] Gene exp ession X
2016 Ani ha e al. [198] Gene exp ession X
2017 Padilha e al. [26] Gene exp ession X X
2018 Biswal e al. [199] Gene exp ession X
2018 Aouabed e al. [200] Biomedical X
2019 Xie e al. [6] Biological and biomedical X
2021 Nicholls e al. [27] Gene exp ession X
2021 Sozdinle , [189] Gene exp ession X
2022 No onha e al. [146] Da a mining and gene exp ession X
2022 José-Ga cía e al. [22] Gene exp ession X
2024 Cas anho e al. [28] Biological and biomedical X X

A.López-Fe nández e al.
1123 Page 38 o 52
Table 3 Reposi o ies whe e he sou ce codes o he HPC biclus e ing algo i hms a e s o ed
Yea Me hod Sou ce code URL
2007 RoBA [74] –
2008 DisCo [107] –
2008 P-Biclus e [77] –
2009 A.Nisa e al [80] –
2012 FLOC [113] –
2013 GBC [17] –
2014 GBC [117] –
2014 MCC [106] –
2014 BiTM-MR [100]h ps:// gi hub. com/ Tugdu alSa azin/ spa k- clus e ing
2014 Cloudnm [101]h p:// admis. udan. edu. cn/ p oje c s/ Cloud NMF. h ml
2015 NMF [118]h ps:// gi hub. com/ bioin o- cnb/ bionm - gpu
2015 Bha naga e al. [108] –
2015 MFCM [83] –
2016 MR-GABiT [104] –
2017 CCS [120]h ps:// gi hub. com/ abha a3/ Condi ion- depen den -
Co e la ion- Subg oups- CCS
2017 PBD-SPEA2 [96] -
2018 EBIC [121]h ps:// gi hub. com/ Epis asisL ab/ ebic
2018 Runibic [85]h ps:// www. bioco nduc o . o g/ packa ges/ elea se/
bioc/ h ml/ unib ic. h ml
2018 Pa BiBi [88]h ps:// sou c e o ge. ne / p oje c s/ pa bi bi /
2019 CuBiBi [122]h ps:// sou c e o ge. ne / p oje c s/ cubib i
2019 SP-PLSS [4] –
2021 ScalaPa BiBi [91]h ps:// gi hub. com/ agu ela/ Scala Pa Bi Bi
2021 COBRAC [92]h ps:// gi hub. com/ haidyi/ c xbi clus
2021 gBiBi [90]h ps:// gi hub. com/ au el iol d ez/ gbibi
Biclus e ing inbioin o ma ics using big da a andHigh… Page 39 o 52 1123
Table 4 Main ea u es o biclus e ing algo i hms accele a ed by adi ional pa allel and dis ibu ed mod-
els
Da a compa ibili y
Yea Me hod Da ase s Compa a i e Noise Da a Bina y Disc e e Con inuous
2007 RoBA [74] 2 0 No Mic oa ays X
2008 P-Biclus e
[77]
1 1 No Mic oa ays X X
2009 Nisa e al [80] 0 0 Yes Mic oa ays X
2015 MFCM [83] 1 2 No Mic oa ays X
2017 PBD-SPEA2
[96]
2 1 Yes Mic oa ays X
2018 Runibic [85] 1 1 Yes RNA-Seq X X
2018 Pa BiBi [88] 2 1 No Mic oa ays X
2021 ScalaPa BiBi
[91]
0 2 No Mic oa ays X
2021 COBRAC [92] 1 0 Yes Mic oa ays X
2022 ARBic [94] 2 1 No Mic oa ays X
2023 EnsemBic [95] 1 1 Yes Mic oa ays &
RNA-Seq
X
Table 5 Compu a ional ea u es o biclus e ing algo i hms based on adi ional pa allel and dis ibu ed
models
Yea Me hod Language Accele a ion Op imisa ion Com-
munica-
ions
2007 RoBA [74] MATLAB MPI No Yes
2008 P-Biclus e [77] ANSI C MPI No No
2009 A.Nisa e al [80] C/C++ MPI Yes Yes
2015 MFCM [83] MATLAB MPI No No
2017 PBD-SPEA2 [96] - - - -
2018 Runibic [85] C/C++ OpenMP No No
2018 Pa BiBi [88] C/C++ MPI/POSIX No Yes
2021 ScalaPa BiBi [91] C/C++ MPI/POSIX Yes Yes
2021 COBRAC [92] C/C++ OpenMP Yes No
2022 ARBic [94] C/C++ OpenMP No No
2023 EnsemBic [95] R - - -
A.López-Fe nández e al.
1123 Page 40 o 52
Table 6 Main ea u es o biclus e ing algo i hms suppo ed by MapReduce pla o ms
Da a compa ibili y
Yea Me hod Pla o m Da ase s Compa a i e Noise Da a Bina y Disc e e Con inuous
2008 DisCo [107] Hadoop 1 0 No - X
2014 MCC [106] - 1 1 No Mic oa ays X
2014 BiTM-MR [100] Spa k 0 0 No - X
2014 Cloudnm [101] Hadoop 2 0 No PPI X
2015 Bha naga e al. [108] Hadoop 1 2 No - X
2016 MR-GABiT [104] MATLAB 1 0 No Mic oa ays X X
2019 SP-PLSS [4] Spa k 2 2 No – X X
Biclus e ing inbioin o ma ics using big da a andHigh… Page 41 o 52 1123
Table 7 Compu a ional ea u es o biclus e ing algo i hms based on MapReduce pla o ms
Yea Me hod MR Jobs Pa i ioning I/O In-memo y Asyn-
ch o-
nous
Speedup Scalabili y
2008 DisCo [107] 1 0 0 0 0 1 3
2014 MCC [106] 1 0 0 0 0 0 1
2014 BiTM-MR [100] 1 0 1 1 1 1 3
2014 Cloudnm [101] 1 0 0 0 0 0 1
2015 Bha naga e al. [108] 1 0 0 0 0 0 3
2016 MR-GABiT [104] 0 1 0 0 0 1 1
2019 SP-PLSS [4] 1 1 1 1 0 1 3
Table 8 Main ea u es o biclus e ing algo i hms suppo ed by GPGPU pla o ms
Da a compa ibili y
Yea Me hod Da ase s Compa a i e Noise Da a Bina y Disc e e Con inuous
2012 FLOC [113] 0 1 No Mic oa ays X X
2013 GBC [17] 1 2 No Mic oa ays X
2014 GBC [117] 1 2 No Mic oa ays X
2015 NMF [118] 1 1 No Mic oa ays X
2017 CCS [120] 2 1 No Mic oa ays X X
2018 EBIC [121] 2 2 Yes Mic oa ays X X X
2019 CuBiBi [122] 0 1 No Mic oa ays X
2021 gBiBi [90] 2 2 No Mic oa ays &
RNA-Seq
X
Table 9 Compu a ional ea u es o biclus e ing algo i hms based on GPGPU pla o ms
Yea Me hod mul i-GPU Sha ed
mem.
Coalescing Alloca ion T ans e Occupancy Sync.
2012 FLOC [113]No No No 0 Global Low No
2013 GBC [17]No Yes Yes 1 Global Low No
2014 GBC [117]No Yes Yes 1 Global High Yes
2015 NMF [118] Yes (MPI) Yes Yes 1 Global High Yes
2017 CCS [120]No Yes No 1 Global Low No
2018 EBIC [121] Yes
(OpenMP)
Yes Yes 1 Global High Yes
2019 CuBiBi
[122]
Yes
(POSIX)
Yes Yes 1 Global Low No
2021 gBiBi [90] Yes
(POSIX)
No Yes 1 Global High No
A.López-Fe nández e al.
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Publishe ’s No e Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in published maps
and ins i u ional a ilia ions.
A.López-Fe nández e al.
1123 Page 52 o 52
Au ho s and A ilia ions
Au elioLópez‑Fe nández4· F anciscoA.Gomez‑Vela1·
DomingoS.Rod iguez‑Baena1· Fe nandoM.Delgado‑Cha es2·
Jo geGonzalez‑Dominguez3
* Au elio López-Fe nández
[email p o ec ed]
F ancisco A. Gomez-Vela
[email p o ec ed]
Domingo S. Rod iguez-Baena
[email p o ec ed]
Fe nando M. Delgado-Cha es
e nando.miguel.delgado-cha es@uni-hambu g.de
Jo ge Gonzalez-Dominguez
jo g[email p o ec ed]
1 In elligen Da a Analysis G oup (DATAi), Uni e sidad Pablo de Ola ide, C a. U e a, km. 1,
ES-41013Se ille, Spain
2 Ins i u e o Compu a ional Sys ems Biology, Uni e si y o Hambu g, No kes asse 9,
22607Hambu g, Ge many
3 Compu e A chi ec u e G oup, Uni e sidade da Co uña, Campus de El iña, 15071ACo uña,
Spain
4 Dp o. Lenguajes y Sis emas In o má icos, Uni e sidad de Se illa, Se ille, Spain