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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:BMC bioinformatics Jg. 23; H. 1; S. 514 - 20
Hauptverfasser: Moreno, Marta, Vilaça, Ricardo, Ferreira, Pedro G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 30.11.2022
Springer Nature B.V
BMC
Schlagworte:
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
ISSN:1471-2105, 1471-2105
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-05065-3