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Comparison of Machine Learning Pipelines for Gene Expression Matrices

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Advances in Bioinformatics and Computational Biology (BSB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13523))

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Abstract

Advances in genetic sequencing technologies have enabled the understanding of the course of diseases in a manner like never before. These technologies produce a data structure called an expression matrix, which contains gene expression values taken under certain sampling conditions. In this paper, we present preliminary work on comparing the application of different machine learning pipelines to an expression matrix. As a case study, we consider a dataset from the Gene Expression Omnibus containing gene expression levels (obtained through scRNA-seq) in the context of Breast Cancer disease. We present a generalized processing pipeline instantiation and discuss the corresponding results.

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Notes

  1. 1.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161529.

  2. 2.

    https://github.com/MLRG-CEFET-RJ/brca-scrna-seq/tree/BSB2022.

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Authors and Affiliations

  1. Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ), Rio de Janeiro, Brazil

    Mateus Devino, Kele Belloze & Eduardo Bezerra

Authors
  1. Mateus Devino
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  2. Kele Belloze
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  3. Eduardo Bezerra
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Corresponding author

Correspondence to Eduardo Bezerra .

Editor information

Editors and Affiliations

  1. Instituto Nacional de Câncer, Rio de Janeiro, Brazil

    Nicole M. Scherer

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Raquel C. de Melo-Minardi

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Devino, M., Belloze, K., Bezerra, E. (2022). Comparison of Machine Learning Pipelines for Gene Expression Matrices. In: Scherer, N.M., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2022. Lecture Notes in Computer Science(), vol 13523. Springer, Cham. https://doi.org/10.1007/978-3-031-21175-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-21175-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21174-4

  • Online ISBN: 978-3-031-21175-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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