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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36637211", "kind": "science-record", "revision": 3, "content_hash": "sha256:6f11ad41b210659b9d6cae6cc357efbd928a0328c32f97c889d84672feca79d6", "created_at": "2026-11-01T19:01:11Z", "updated_at": "2026-11-01T22:08:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Dealing with dimensionality: the application of machine learning to multi-omics data.", "doi": "10.1093/bioinformatics/btad021", "pmid": "36637211", "authors": ["Dylan Feldner-Busztin", "Panos Firbas Nisantzis", "Shelley Jane Edmunds", "Gergely Boza", "Fernando Racimo", "Shyam Gopalakrishnan", "Morten Tønsberg Limborg", "Leo Lahti", "Gonzalo G de Polavieja"], "journal": "Bioinformatics (Oxford, England)", "year": 2023, "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.\nRESULTS: 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.\nAVAILABILITY 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.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "Using Fisher’s exact test, we tested for a statistically significant difference in the number of appearances of each technique in the multi-omics group versus the general ML group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36637211", "locator": "sections[9] \"Which ML techniques were used?\" paragraph 3", "quote": "Using Fisher’s exact test, we tested for a statistically significant difference in the number of appearances of each technique in the multi-omics group versus the general ML group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "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.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36637211", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "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."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The methods used are those that address the challenges of datasets with few samples and many features.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36637211", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The methods used are those that address the challenges of datasets with few samples and many features.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36637211"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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