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Brechtmann, F.* ; Bechtler, T.* ; Londhe, S.* ; Mertes, C.* ; Gagneur, J.

Evaluation of input data modality choices on functional gene embeddings.

NAR Gen. Bioinfo. 5:lqad095 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Functional gene embeddings, numerical vectors capturing gene function, provide a promising way to integrate functional gene information into machine learning models. These embeddings are learnt by applying self-supervised machine-learning algorithms on various data types including quantitative omics measurements, protein-protein interaction networks and literature. However, downstream evaluations comparing alternative data modalities used to construct functional gene embeddings have been lacking. Here we benchmarked functional gene embeddings obtained from various data modalities for predicting disease-gene lists, cancer drivers, phenotype-gene associations and scores from genome-wide association studies. Off-the-shelf predictors trained on precomputed embeddings matched or outperformed dedicated state-of-the-art predictors, demonstrating their high utility. Embeddings based on literature and protein-protein interactions inferred from low-throughput experiments outperformed embeddings derived from genome-wide experimental data (transcriptomics, deletion screens and protein sequence) when predicting curated gene lists. In contrast, they did not perform better when predicting genome-wide association signals and were biased towards highly-studied genes. These results indicate that embeddings derived from literature and low-throughput experiments appear favourable in many existing benchmarks because they are biased towards well-studied genes and should therefore be considered with caution. Altogether, our study and precomputed embeddings will facilitate the development of machine-learning models in genetics and related fields.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Epilepsy; Map
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2631-9268
e-ISSN 2631-9268
Zeitschrift NAR: Genomics and Bioinformatics
Quellenangaben Band: 5, Heft: 4, Seiten: , Artikelnummer: lqad095 Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen This research has been conducted using summary statistics produced by genebass (under application 26041 and 48511) and the Pan-UKB project generated from the UK Biobank resource.
DOI 10.1093/nargab/lqad095
WOS ID 001098611700004
Scopus ID 85178317535
PubMed ID 37942285
Erfassungsdatum 2023-11-28
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