Biclustering fMRI time series: a comparative study.
Biclustering
Neurosciences
Time series analysis
fMRI
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
23 May 2022
23 May 2022
Historique:
received:
27
04
2021
accepted:
13
05
2022
entrez:
23
5
2022
pubmed:
24
5
2022
medline:
26
5
2022
Statut:
epublish
Résumé
The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.
Sections du résumé
BACKGROUND
BACKGROUND
The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain.
RESULTS
RESULTS
This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis.
CONCLUSIONS
CONCLUSIONS
This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.
Identifiants
pubmed: 35606701
doi: 10.1186/s12859-022-04733-8
pii: 10.1186/s12859-022-04733-8
pmc: PMC9126639
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
192Subventions
Organisme : Fundação para a Ciência e a Tecnologia
ID : 2021.07810.BD
Organisme : Fundação para a Ciência e a Tecnologia
ID : PTDC/CCI-CIF/4613/2020
Organisme : Fundação para a Ciência e a Tecnologia
ID : PTDC/EME-SIS/31474/2017
Informations de copyright
© 2022. The Author(s).
Références
J Biomed Inform. 2015 Oct;57:163-80
pubmed: 26160444
Am J Psychiatry. 2007 Mar;164(3):450-7
pubmed: 17329470
Bioinformatics. 2005 Oct 15;21(20):3840-5
pubmed: 16144809
Neuroimage. 1999 Mar;9(3):298-310
pubmed: 10075900
Neuroimage. 2008 Jan 1;39(1):527-37
pubmed: 17919929
IEEE Trans Biomed Eng. 2011 Dec;58(12):3406-17
pubmed: 21900068
Neuroimage. 2010 Oct 1;52(4):1465-76
pubmed: 20553896
Genome Res. 2003 Apr;13(4):703-16
pubmed: 12671006
Dis Markers. 2013;35(1):3-9
pubmed: 24167344
Bioinformatics. 2020 Feb 15;36(4):1143-1149
pubmed: 31503285
J Integr Bioinform. 2012 Jul 24;9(3):207
pubmed: 22829578
J Integr Bioinform. 2011 Sep 15;8(3):175
pubmed: 21926438
Neuroimage. 2005 Mar;25(1):193-205
pubmed: 15734355
Eur Neuropsychopharmacol. 2010 Aug;20(8):519-34
pubmed: 20471808
Mol Neurodegener. 2019 Jun 7;14(1):21
pubmed: 31174557
Bioinformatics. 2006 May 1;22(9):1122-9
pubmed: 16500941
Neuroimage. 2010 May 1;50(4):1690-701
pubmed: 20079856
BMC Bioinformatics. 2017 Feb 2;18(1):82
pubmed: 28153040
Neuroimage. 2006 Feb 15;29(4):1359-67
pubmed: 16260155
Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103
pubmed: 10977070
BMC Res Notes. 2009 Jul 07;2:124
pubmed: 19583847
Neuron. 2009 Apr 16;62(1):42-52
pubmed: 19376066
Bioinformatics. 2015 Jun 15;31(12):i17-26
pubmed: 26072479
IEEE Trans Med Imaging. 2004 Feb;23(2):137-52
pubmed: 14964560
Algorithms Mol Biol. 2009 Jun 04;4:8
pubmed: 19497096
Philos Trans A Math Phys Eng Sci. 2012 Dec 31;371(1984):20110534
pubmed: 23277597
Schizophr Bull. 2007 Jul;33(4):1004-12
pubmed: 17556752
Pac Symp Biocomput. 2003;:77-88
pubmed: 12603019
Neuroimage. 2012 Feb 15;59(4):4160-7
pubmed: 22178299
Front Neurosci. 2014 Jul 01;8:167
pubmed: 25071425
Hum Brain Mapp. 2005 Dec;26(4):231-9
pubmed: 15954139
IEEE/ACM Trans Comput Biol Bioinform. 2004 Jan-Mar;1(1):24-45
pubmed: 17048406
J Neurosci Methods. 2019 May 15;320:64-71
pubmed: 30902651
Neuroimage. 2012 Aug 15;62(2):782-90
pubmed: 21979382
Neuroimage. 2017 Feb 15;147:736-745
pubmed: 27865923
Brief Bioinform. 2019 Jul 19;20(4):1449-1464
pubmed: 29490019
BMC Bioinformatics. 2006 Feb 17;7:78
pubmed: 16503973
Neuroimage. 2006 Jul 1;31(3):968-80
pubmed: 16530430
Proc Natl Acad Sci U S A. 2006 Sep 12;103(37):13848-53
pubmed: 16945915
Biol Psychiatry. 2007 Sep 1;62(5):429-37
pubmed: 17210143
Front Psychiatry. 2017 Sep 26;8:179
pubmed: 29018368
MAGMA. 2010 Dec;23(5-6):289-307
pubmed: 20972883
Neuroimage. 2010 Sep;52(3):1059-69
pubmed: 19819337
Philos Trans R Soc Lond B Biol Sci. 2005 May 29;360(1457):1001-13
pubmed: 16087444
IEEE/ACM Trans Comput Biol Bioinform. 2014 Sep-Oct;11(5):942-54
pubmed: 26356865
Bioinformatics. 2010 Jun 15;26(12):1520-7
pubmed: 20418340
Neuroimage. 2004;23 Suppl 1:S208-19
pubmed: 15501092
Hum Brain Mapp. 2015 Feb;36(2):756-67
pubmed: 25394864
Sci Rep. 2019 Jun 21;9(1):9043
pubmed: 31227769
PLoS One. 2015 Mar 12;10(3):e0115497
pubmed: 25763839
Proc Natl Acad Sci U S A. 2009 Jan 27;106(4):1279-84
pubmed: 19164577
Hum Brain Mapp. 2009 Dec;30(12):3865-86
pubmed: 19507160
J Bioinform Comput Biol. 2009 Oct;7(5):853-68
pubmed: 19785049
IEEE Trans Biomed Eng. 2020 Jan;67(1):110-121
pubmed: 30946659
Neuroimage. 1995 Sep;2(3):173-81
pubmed: 9343600
Neuroimage. 2010 Apr 15;50(3):1027-35
pubmed: 20060479
Comput Biol Med. 2012 Feb;42(2):245-56
pubmed: 22196882
Exp Neurol. 2009 May;217(1):147-53
pubmed: 19416664
Neuroimage Clin. 2013 Aug 07;3:123-31
pubmed: 24179856
Brief Bioinform. 2013 May;14(3):279-92
pubmed: 22772837
Front Hum Neurosci. 2013 Jul 05;7:313
pubmed: 23847492
Algorithms Mol Biol. 2014 Dec 16;9(1):27
pubmed: 25649207
Nucleic Acids Res. 2009 Aug;37(15):e101
pubmed: 19509312
BMC Bioinformatics. 2017 Jan 23;18(1):55
pubmed: 28114903
IEEE/ACM Trans Comput Biol Bioinform. 2010 Jan-Mar;7(1):153-65
pubmed: 20150677
Neuroimage. 2012 Feb 15;59(4):4141-59
pubmed: 22019879
Curr Opin Neurobiol. 2016 Apr;37:12-15
pubmed: 26752736
Neuroimage. 2009 Mar;45(1 Suppl):S173-86
pubmed: 19059349
Bioinformatics. 2002;18 Suppl 1:S136-44
pubmed: 12169541
Neuroimage. 2011 Feb 14;54(4):2950-9
pubmed: 20974260
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902
pubmed: 12689096
Hum Brain Mapp. 2009 Jan;30(1):256-66
pubmed: 18041738
Proc Natl Acad Sci U S A. 2004 Mar 30;101(13):4637-42
pubmed: 15070770
Hum Brain Mapp. 2008 Jul;29(7):818-27
pubmed: 18438889
Brain. 2008 Apr;131(Pt 4):945-61
pubmed: 18299296