Dataset Information

Dealing with dimensionality: the application of machine learning to multi-omics data.

ABSTRACT:

Motivation

Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets.

Results

Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments.

Availability and implementation

All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Feldner-Busztin D

PROVIDER: S-EPMC9907220 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Dealing with dimensionality: the application of machine learning to multi-omics data.

Feldner-Busztin Dylan D Firbas Nisantzis Panos P Edmunds Shelley Jane SJ Boza Gergely G Racimo Fernando F Gopalakrishnan Shyam S Limborg Morten Tønsberg MT Lahti Leo L de Polavieja Gonzalo G GG

Bioinformatics (Oxford, England) 20230201 2

<h4>Motivation</h4>Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature ...[more]

PMID: 36637211

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Dataset Information

Dealing with dimensionality: the application of machine learning to multi-omics data.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Dealing with dimensionality: the application of machine learning to multi-omics data.

Similar Datasets

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