Scalable transcriptomics analysis with Dask: applications in data science and machine learning

Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications i...

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Published inBMC bioinformatics Vol. 23; no. 1; pp. 514 - 20
Main Authors Moreno, Marta, Vilaça, Ricardo, Ferreira, Pedro G.
Format Journal Article
LanguageEnglish
Published London BioMed Central 30.11.2022
Springer Nature B.V
BMC
Subjects
Algorithms
Bioinformatics
Biological effects
Biomedical and Life Sciences
Case studies
Computational Biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Data analysis
Data Science
Disease
DNA methylation
Feature selection
Gene expression
Gene Expression Profiling
Genetic engineering
Genomics
Genotype & phenotype
Learning algorithms
Life Sciences
Machine Learning
Medical research
Microarrays
Parallel programming
Pipelining (computers)
Programming languages
Research Article
Scalable data science
Transcriptome
Transcriptomics
Scalable data science
Data analysis
Gene expression
Transcriptomics
Machine learning
Online AccessGet full text
ISSN1471-2105
1471-2105
DOI10.1186/s12859-022-05065-3

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Abstract Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. Methods In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. Results This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask . Conclusion By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
AbstractList Abstract Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. Methods In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. Results This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask . Conclusion By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. Methods In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. Results This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask. Conclusion By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask . By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary.BACKGROUNDGene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary.In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics.METHODSIn this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics.This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask .RESULTSThis review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask .By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.CONCLUSIONBy showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. Methods In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. Results This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask . Conclusion By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
ArticleNumber 514
Author Vilaça, Ricardo
Moreno, Marta
Ferreira, Pedro G.
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  givenname: Pedro G.
  surname: Ferreira
  fullname: Ferreira, Pedro G.
  email: pgferreira@fc.up.pt
  organization: Department of Computer Science, Faculty of Sciences, University of Porto, Laboratory of Artificial Intelligence and Decision Support, INESC TEC, Institute of Molecular Pathology and Immunology of the University of Porto, Institute for Research and Innovation in Health (i3s)
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crossref_primary_10_59717_j_xinn_life_2024_100079
Cites_doi 10.1038/s41388-018-0385-y
10.1016/s2213-2600(16)00048-5
10.1021/acs.molpharmaceut.6b00248
10.3322/caac.21565
10.1093/bioinformatics/btz420
10.1007/978-1-4842-2199-0_8
10.1038/nature14248
10.3390/info11040193
10.1016/j.celrep.2019.11.017
10.1109/icpads.2012.97
10.1038/npre.2010.4282.1
10.1126/science.aad0095
10.1093/bioinformatics/btu324
10.1145/106973.106981
10.1245/s10434-013-3070-y
10.1038/nature12625
10.1038/nmeth.1613
10.1007/s10120-019-01029-4
10.1186/gb-2010-11-3-r25
10.3390/ijms18081652
10.1038/s41592-019-0686-2
10.14778/3407790.3407807
10.5114/wo.2014.47136
10.1038/89044
10.1051/0004-6361/201732493
10.1109/works49585.2019.00010
10.1146/annurev-immunol-090419-020340
10.1038/nature11247
10.1038/s41573-019-0024-5
10.1145/2939672.2939785
10.1101/2021.03.23.436606
10.1093/bioinformatics/18.1.39
10.1101/2022.05.26.493607
10.1101/2021.05.03.442499
10.1109/IPDPSW55747.2022.00045
10.7554/elife.51254
10.1164/rccm.201502-0355oc
10.1016/j.cell.2016.12.013
10.1038/s12276-018-0071-8
10.25080/majora-1b6fd038-011
10.1038/s41592-021-01358-2
10.1186/1471-2105-11-94
10.1038/nrg.2016.10
10.1126/science.aaa8415
10.1161/circulationaha.115.001593
10.1016/j.jaad.2015.01.009
10.14778/3494124.3494152
10.1109/mcse.2007.55
10.1101/2021.02.24.432740
10.1093/nar/gkx1081
10.1038/nmeth.1226
10.1186/s12864-017-3906-0
10.1186/s13059-018-1599-6
10.1186/s13059-019-1906-x
10.1186/s13059-019-1836-7
10.1038/nrg3920
10.25080/shinma-7f4c6e7-00a
10.1145/2934664
10.1186/s12859-017-1984-2
10.1038/s41586-020-2649-2
10.25080/Majora-629e541a-007
10.1126/scitranslmed.aal5209
10.1371/journal.pone.0186906
10.1093/bioinformatics/bty916
10.1038/nmeth.4220
10.1056/nejmoa1510764
10.1182/blood-2013-11-536359
10.1093/infdis/jis499
10.1111/j.2517-6161.1974.tb00994.x
10.1038/s41576-019-0150-2
10.1038/ng.2653
10.21105/joss.03249
10.7314/apjcp.2016.17.2.835
10.1155/2015/621690
10.1021/acs.jctc.1c00447
10.1145/2503210.2503219
10.1038/nature12531
10.1038/s41598-020-58290-2
10.1186/s12920-020-0677-2
10.1186/gb-2013-14-7-r75
10.1172/jci.insight.93247
10.25080/majora-7b98e3ed-013
10.15252/msb.20188746
10.1002/cam4.718
10.25080/majora-92bf1922-00a
10.1515/jib-2014-236
10.1038/s41588-018-0160-6
10.1145/3225058.3225128
10.1093/bib/bbx008
10.1109/2.476197
10.1038/s41596-020-00409-w
10.1109/mcse.2010.119
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Issue 1
Keywords Scalable data science
Data analysis
Gene expression
Transcriptomics
Machine learning
Language English
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References M List (5065_CR19) 2014; 11
V Svensson (5065_CR87) 2017; 14
J Lonsdale (5065_CR92) 2013; 45
H El-Rewini (5065_CR64) 1995; 28
E Madissoon (5065_CR90) 2020; 21
5065_CR48
5065_CR47
5065_CR46
MD Robinson (5065_CR29) 2010; 11
D Petersohn (5065_CR99) 2020; 13
M Kunz (5065_CR72) 2018; 37
5065_CR42
5065_CR41
CR Harris (5065_CR36) 2020; 585
MA Breddels (5065_CR103) 2018; 618
5065_CR102
5065_CR101
JH Bullard (5065_CR30) 2010; 11
J Vamathevan (5065_CR9) 2019; 18
5065_CR38
MI Jordan (5065_CR16) 2015; 349
J Zhou (5065_CR21) 2018; 50
5065_CR33
M Mostavi (5065_CR74) 2020; 13
G Palla (5065_CR59) 2022; 19
C Soneson (5065_CR65) 2014; 30
S-K Kim (5065_CR73) 2020; 23
CA Davis (5065_CR95) 2018; 46
ENCODE Project Consortium (5065_CR94) 2012; 489
P Gerami (5065_CR78) 2015; 72
M Garber (5065_CR27) 2011; 8
EMV Allen (5065_CR79) 2015; 350
D Petersohn (5065_CR100) 2021; 15
J Iqbal (5065_CR77) 2014; 123
F Wrede (5065_CR53) 2019; 35
5065_CR62
5065_CR61
H-Y Lim (5065_CR84) 2013; 20
A Casamassimi (5065_CR2) 2017; 18
J Zyprych-Walczak (5065_CR28) 2015; 2015
M Zaharia (5065_CR44) 2012; 37
K Tomczak (5065_CR85) 2015; 19
T Lappalainen (5065_CR93) 2013; 501
S Anders (5065_CR31) 2010
Y Li (5065_CR71) 2017; 18
C Evans (5065_CR32) 2018; 19
JC Daniel (5065_CR43) 2019
RA Burrell (5065_CR66) 2013; 501
5065_CR58
5065_CR57
5065_CR52
5065_CR51
MW Libbrecht (5065_CR15) 2015; 16
P Virtanen (5065_CR37) 2020; 17
D Glass (5065_CR6) 2013; 14
J Bergstra (5065_CR34) 2012; 13
JM Cliff (5065_CR10) 2013; 207
BP Scicluna (5065_CR4) 2015; 192
NC Tran (5065_CR55) 2021; 6
MD Lam (5065_CR63) 1991; 25
MD Podolsky (5065_CR80) 2016; 17
SA Byron (5065_CR1) 2016; 17
RD Team (5065_CR60) 2018
C Huang (5065_CR12) 2017; 12
JD Hunter (5065_CR39) 2007; 9
A Taylor-Weiner (5065_CR50) 2019; 20
M Efremova (5065_CR86) 2020; 38
T Sakellaropoulos (5065_CR13) 2019; 29
TS Andrews (5065_CR89) 2021; 16
S-J Sammut (5065_CR3) 2021; 601
T Kang (5065_CR17) 2017; 18
MD Luecken (5065_CR88) 2019; 15
JE Bradner (5065_CR67) 2017; 168
A Kundaje (5065_CR96) 2015; 518
A Aliper (5065_CR18) 2016; 13
5065_CR76
KD Miller (5065_CR69) 2019; 69
J Khan (5065_CR75) 2001; 7
S Raschka (5065_CR23) 2020; 11
T Moerman (5065_CR56) 2019; 35
M Zaharia (5065_CR45) 2016; 59
M Stone (5065_CR24) 1974; 36
F Perez (5065_CR35) 2010; 13
CA Jackson (5065_CR54) 2020; 9
JA Sparano (5065_CR83) 2015; 373
5065_CR25
5065_CR22
DV Nguyen (5065_CR70) 2002; 18
5065_CR20
N Wong (5065_CR81) 2016; 5
A Mortazavi (5065_CR26) 2008; 5
JG Fleischer (5065_CR7) 2018; 19
B Hwang (5065_CR97) 2018; 50
RC Deo (5065_CR14) 2015; 132
F Pedregosa (5065_CR40) 2011; 12
5065_CR98
M Sinkala (5065_CR82) 2020; 10
PG Murray (5065_CR11) 2018
5065_CR91
R Stark (5065_CR68) 2019; 20
P Smith (5065_CR49) 2021; 17
BB Cummings (5065_CR8) 2017; 9
TE Sweeney (5065_CR5) 2016; 4
References_xml – volume: 37
  start-page: 6136
  issue: 47
  year: 2018
  ident: 5065_CR72
  publication-title: Oncogene
  doi: 10.1038/s41388-018-0385-y
– volume: 4
  start-page: 213
  issue: 3
  year: 2016
  ident: 5065_CR5
  publication-title: Lancet Respir Med
  doi: 10.1016/s2213-2600(16)00048-5
– volume: 13
  start-page: 2524
  issue: 7
  year: 2016
  ident: 5065_CR18
  publication-title: Mol Pharm
  doi: 10.1021/acs.molpharmaceut.6b00248
– volume: 601
  start-page: 1
  year: 2021
  ident: 5065_CR3
  publication-title: Nature
– volume: 69
  start-page: 363
  issue: 5
  year: 2019
  ident: 5065_CR69
  publication-title: CA Cancer J Clin
  doi: 10.3322/caac.21565
– volume: 35
  start-page: 5199
  issue: 24
  year: 2019
  ident: 5065_CR53
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btz420
– ident: 5065_CR91
  doi: 10.1007/978-1-4842-2199-0_8
– volume: 518
  start-page: 317
  issue: 7539
  year: 2015
  ident: 5065_CR96
  publication-title: Nature
  doi: 10.1038/nature14248
– ident: 5065_CR33
– volume: 11
  start-page: 193
  issue: 4
  year: 2020
  ident: 5065_CR23
  publication-title: Information
  doi: 10.3390/info11040193
– volume: 29
  start-page: 3367
  issue: 11
  year: 2019
  ident: 5065_CR13
  publication-title: Cell Rep
  doi: 10.1016/j.celrep.2019.11.017
– ident: 5065_CR41
  doi: 10.1109/icpads.2012.97
– year: 2010
  ident: 5065_CR31
  publication-title: Nat Preced
  doi: 10.1038/npre.2010.4282.1
– volume: 350
  start-page: 207
  issue: 6257
  year: 2015
  ident: 5065_CR79
  publication-title: Science
  doi: 10.1126/science.aad0095
– volume: 30
  start-page: 2517
  issue: 17
  year: 2014
  ident: 5065_CR65
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btu324
– volume: 25
  start-page: 63
  issue: Special Issue
  year: 1991
  ident: 5065_CR63
  publication-title: ACM SIGOPS Oper Syst Rev
  doi: 10.1145/106973.106981
– volume: 20
  start-page: 3747
  issue: 12
  year: 2013
  ident: 5065_CR84
  publication-title: Ann Surg Oncol
  doi: 10.1245/s10434-013-3070-y
– volume: 13
  start-page: 281
  issue: 2
  year: 2012
  ident: 5065_CR34
  publication-title: J Mach Learn Res
– volume: 37
  start-page: 45
  issue: 4
  year: 2012
  ident: 5065_CR44
  publication-title: Usenix Login
– volume: 501
  start-page: 338
  issue: 7467
  year: 2013
  ident: 5065_CR66
  publication-title: Nature
  doi: 10.1038/nature12625
– ident: 5065_CR76
– volume: 8
  start-page: 469
  issue: 6
  year: 2011
  ident: 5065_CR27
  publication-title: Nat Methods
  doi: 10.1038/nmeth.1613
– volume: 23
  start-page: 473
  issue: 3
  year: 2020
  ident: 5065_CR73
  publication-title: Gastric Cancer
  doi: 10.1007/s10120-019-01029-4
– volume: 11
  start-page: 1
  issue: 3
  year: 2010
  ident: 5065_CR29
  publication-title: Genome Biol
  doi: 10.1186/gb-2010-11-3-r25
– volume: 18
  start-page: 1652
  issue: 8
  year: 2017
  ident: 5065_CR2
  publication-title: Int J Mol Sci
  doi: 10.3390/ijms18081652
– volume: 17
  start-page: 261
  issue: 3
  year: 2020
  ident: 5065_CR37
  publication-title: Nat Methods
  doi: 10.1038/s41592-019-0686-2
– volume: 13
  start-page: 2033
  issue: 12
  year: 2020
  ident: 5065_CR99
  publication-title: VLDB Endow
  doi: 10.14778/3407790.3407807
– volume: 19
  start-page: 68
  issue: 1A
  year: 2015
  ident: 5065_CR85
  publication-title: Współczesna Onkologia
  doi: 10.5114/wo.2014.47136
– ident: 5065_CR101
– volume-title: Data science at scale with Python and Dask
  year: 2019
  ident: 5065_CR43
– volume: 7
  start-page: 673
  issue: 6
  year: 2001
  ident: 5065_CR75
  publication-title: Nat Med
  doi: 10.1038/89044
– volume: 618
  start-page: 13
  year: 2018
  ident: 5065_CR103
  publication-title: Astron Astrophys
  doi: 10.1051/0004-6361/201732493
– ident: 5065_CR52
  doi: 10.1109/works49585.2019.00010
– volume: 38
  start-page: 727
  year: 2020
  ident: 5065_CR86
  publication-title: Annu Rev Immunol
  doi: 10.1146/annurev-immunol-090419-020340
– volume: 489
  start-page: 57
  issue: 7414
  year: 2012
  ident: 5065_CR94
  publication-title: Nature
  doi: 10.1038/nature11247
– volume: 18
  start-page: 463
  issue: 6
  year: 2019
  ident: 5065_CR9
  publication-title: Nat Rev Drug Discov
  doi: 10.1038/s41573-019-0024-5
– ident: 5065_CR20
  doi: 10.1145/2939672.2939785
– ident: 5065_CR58
  doi: 10.1101/2021.03.23.436606
– volume: 18
  start-page: 39
  issue: 1
  year: 2002
  ident: 5065_CR70
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/18.1.39
– ident: 5065_CR61
  doi: 10.1101/2022.05.26.493607
– ident: 5065_CR57
  doi: 10.1101/2021.05.03.442499
– ident: 5065_CR62
  doi: 10.1109/IPDPSW55747.2022.00045
– volume: 9
  start-page: 51254
  year: 2020
  ident: 5065_CR54
  publication-title: eLife
  doi: 10.7554/elife.51254
– volume: 192
  start-page: 826
  issue: 7
  year: 2015
  ident: 5065_CR4
  publication-title: Am J Respir Crit Care Med
  doi: 10.1164/rccm.201502-0355oc
– volume: 168
  start-page: 629
  issue: 4
  year: 2017
  ident: 5065_CR67
  publication-title: Cell
  doi: 10.1016/j.cell.2016.12.013
– volume: 50
  start-page: 1
  issue: 8
  year: 2018
  ident: 5065_CR97
  publication-title: Exp Mol Med
  doi: 10.1038/s12276-018-0071-8
– ident: 5065_CR98
  doi: 10.25080/majora-1b6fd038-011
– volume: 19
  start-page: 171
  issue: 2
  year: 2022
  ident: 5065_CR59
  publication-title: Nat Methods
  doi: 10.1038/s41592-021-01358-2
– volume: 11
  start-page: 1
  issue: 1
  year: 2010
  ident: 5065_CR30
  publication-title: BMC Bioinform
  doi: 10.1186/1471-2105-11-94
– volume: 17
  start-page: 257
  issue: 5
  year: 2016
  ident: 5065_CR1
  publication-title: Nat Rev Genet
  doi: 10.1038/nrg.2016.10
– volume: 349
  start-page: 255
  issue: 6245
  year: 2015
  ident: 5065_CR16
  publication-title: Science
  doi: 10.1126/science.aaa8415
– volume: 132
  start-page: 1920
  issue: 20
  year: 2015
  ident: 5065_CR14
  publication-title: Circulation
  doi: 10.1161/circulationaha.115.001593
– volume-title: RAPIDS: collection of libraries for end to end GPU data science
  year: 2018
  ident: 5065_CR60
– volume: 72
  start-page: 780
  issue: 5
  year: 2015
  ident: 5065_CR78
  publication-title: J Am Acad Dermatol
  doi: 10.1016/j.jaad.2015.01.009
– volume: 15
  start-page: 739
  issue: 3
  year: 2021
  ident: 5065_CR100
  publication-title: Proc VLDB Endow
  doi: 10.14778/3494124.3494152
– volume: 9
  start-page: 90
  issue: 3
  year: 2007
  ident: 5065_CR39
  publication-title: Comput Sci Engi
  doi: 10.1109/mcse.2007.55
– ident: 5065_CR51
  doi: 10.1101/2021.02.24.432740
– volume: 46
  start-page: 794
  issue: D1
  year: 2018
  ident: 5065_CR95
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkx1081
– volume: 5
  start-page: 621
  issue: 7
  year: 2008
  ident: 5065_CR26
  publication-title: Nat Methods
  doi: 10.1038/nmeth.1226
– volume: 18
  start-page: 1
  issue: 1
  year: 2017
  ident: 5065_CR71
  publication-title: BMC Genom
  doi: 10.1186/s12864-017-3906-0
– volume: 19
  start-page: 1
  issue: 1
  year: 2018
  ident: 5065_CR7
  publication-title: Genome Biol
  doi: 10.1186/s13059-018-1599-6
– volume: 21
  start-page: 1
  issue: 1
  year: 2020
  ident: 5065_CR90
  publication-title: Genome Biol
  doi: 10.1186/s13059-019-1906-x
– volume: 20
  start-page: 1
  issue: 1
  year: 2019
  ident: 5065_CR50
  publication-title: Genome Biol
  doi: 10.1186/s13059-019-1836-7
– volume: 16
  start-page: 321
  issue: 6
  year: 2015
  ident: 5065_CR15
  publication-title: Nat Rev Genet
  doi: 10.1038/nrg3920
– ident: 5065_CR47
  doi: 10.25080/shinma-7f4c6e7-00a
– volume: 59
  start-page: 56
  issue: 11
  year: 2016
  ident: 5065_CR45
  publication-title: Commun ACM
  doi: 10.1145/2934664
– volume: 18
  start-page: 1
  issue: 1
  year: 2017
  ident: 5065_CR17
  publication-title: BMC Bioinform
  doi: 10.1186/s12859-017-1984-2
– volume: 585
  start-page: 357
  issue: 7825
  year: 2020
  ident: 5065_CR36
  publication-title: Nature
  doi: 10.1038/s41586-020-2649-2
– ident: 5065_CR46
  doi: 10.25080/Majora-629e541a-007
– volume: 9
  start-page: 5209
  issue: 386
  year: 2017
  ident: 5065_CR8
  publication-title: Sci Transl Med
  doi: 10.1126/scitranslmed.aal5209
– volume: 12
  start-page: 0186906
  issue: 10
  year: 2017
  ident: 5065_CR12
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0186906
– volume: 35
  start-page: 2159
  issue: 12
  year: 2019
  ident: 5065_CR56
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/bty916
– volume: 14
  start-page: 381
  issue: 4
  year: 2017
  ident: 5065_CR87
  publication-title: Nat Methods
  doi: 10.1038/nmeth.4220
– volume: 373
  start-page: 2005
  issue: 21
  year: 2015
  ident: 5065_CR83
  publication-title: N Engl J Med
  doi: 10.1056/nejmoa1510764
– volume: 123
  start-page: 2915
  issue: 19
  year: 2014
  ident: 5065_CR77
  publication-title: Blood J Am Soc Hematol
  doi: 10.1182/blood-2013-11-536359
– volume: 207
  start-page: 18
  issue: 1
  year: 2013
  ident: 5065_CR10
  publication-title: J Infect Dis
  doi: 10.1093/infdis/jis499
– volume: 36
  start-page: 111
  issue: 2
  year: 1974
  ident: 5065_CR24
  publication-title: J R Stat Soc Ser B (Methodol)
  doi: 10.1111/j.2517-6161.1974.tb00994.x
– volume: 20
  start-page: 631
  issue: 11
  year: 2019
  ident: 5065_CR68
  publication-title: Nat Rev Genet
  doi: 10.1038/s41576-019-0150-2
– volume: 45
  start-page: 580
  issue: 6
  year: 2013
  ident: 5065_CR92
  publication-title: Nat Genet
  doi: 10.1038/ng.2653
– volume: 6
  start-page: 3249
  issue: 61
  year: 2021
  ident: 5065_CR55
  publication-title: J Open Source Softw
  doi: 10.21105/joss.03249
– volume: 17
  start-page: 835
  issue: 2
  year: 2016
  ident: 5065_CR80
  publication-title: Asian Pac J Cancer Prev
  doi: 10.7314/apjcp.2016.17.2.835
– volume: 2015
  start-page: 1
  year: 2015
  ident: 5065_CR28
  publication-title: Biomed Res Int
  doi: 10.1155/2015/621690
– volume: 17
  start-page: 5907
  issue: 9
  year: 2021
  ident: 5065_CR49
  publication-title: J Chem Theory Comput
  doi: 10.1021/acs.jctc.1c00447
– volume: 12
  start-page: 2825
  year: 2011
  ident: 5065_CR40
  publication-title: J Mach Learn Res
– ident: 5065_CR42
  doi: 10.1145/2503210.2503219
– volume: 501
  start-page: 506
  issue: 7468
  year: 2013
  ident: 5065_CR93
  publication-title: Nature
  doi: 10.1038/nature12531
– volume: 10
  start-page: 1
  issue: 1
  year: 2020
  ident: 5065_CR82
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-58290-2
– volume: 13
  start-page: 1
  issue: 5
  year: 2020
  ident: 5065_CR74
  publication-title: BMC Med Genom
  doi: 10.1186/s12920-020-0677-2
– volume: 14
  start-page: 1
  issue: 7
  year: 2013
  ident: 5065_CR6
  publication-title: Genome Biol
  doi: 10.1186/gb-2013-14-7-r75
– year: 2018
  ident: 5065_CR11
  publication-title: JCI Insight
  doi: 10.1172/jci.insight.93247
– ident: 5065_CR22
  doi: 10.25080/majora-7b98e3ed-013
– ident: 5065_CR102
– volume: 15
  start-page: 8746
  issue: 6
  year: 2019
  ident: 5065_CR88
  publication-title: Mol Syst Biol
  doi: 10.15252/msb.20188746
– volume: 5
  start-page: 1619
  issue: 7
  year: 2016
  ident: 5065_CR81
  publication-title: Cancer Med
  doi: 10.1002/cam4.718
– ident: 5065_CR25
– ident: 5065_CR38
  doi: 10.25080/majora-92bf1922-00a
– volume: 11
  start-page: 1
  issue: 2
  year: 2014
  ident: 5065_CR19
  publication-title: J Integr Bioinform
  doi: 10.1515/jib-2014-236
– volume: 50
  start-page: 1171
  issue: 8
  year: 2018
  ident: 5065_CR21
  publication-title: Nat Genet
  doi: 10.1038/s41588-018-0160-6
– ident: 5065_CR48
  doi: 10.1145/3225058.3225128
– volume: 19
  start-page: 776
  issue: 5
  year: 2018
  ident: 5065_CR32
  publication-title: Brief Bioinform
  doi: 10.1093/bib/bbx008
– volume: 28
  start-page: 27
  issue: 12
  year: 1995
  ident: 5065_CR64
  publication-title: Computer
  doi: 10.1109/2.476197
– volume: 16
  start-page: 1
  issue: 1
  year: 2021
  ident: 5065_CR89
  publication-title: Nat Protoc
  doi: 10.1038/s41596-020-00409-w
– volume: 13
  start-page: 13
  issue: 2
  year: 2010
  ident: 5065_CR35
  publication-title: Comput Sci Eng
  doi: 10.1109/mcse.2010.119
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Snippet Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive...
Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the...
Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive...
Abstract Background Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive...
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SubjectTerms Algorithms
Bioinformatics
Biological effects
Biomedical and Life Sciences
Case studies
Computational Biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Data analysis
Data Science
Disease
DNA methylation
Feature selection
Gene expression
Gene Expression Profiling
Genetic engineering
Genomics
Genotype & phenotype
Learning algorithms
Life Sciences
Machine Learning
Medical research
Microarrays
Parallel programming
Pipelining (computers)
Programming languages
Research Article
Scalable data science
Transcriptome
Transcriptomics
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Title Scalable transcriptomics analysis with Dask: applications in data science and machine learning
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