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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)
Publ. Version/Full Text 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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Epilepsy; Map
ISSN (print) / ISBN 2631-9268
e-ISSN 2631-9268
Journal NAR: Genomics and Bioinformatics
Quellenangaben Volume: 5, Issue: 4, Pages: , Article Number: lqad095 Supplement: ,
Publisher Oxford University Press
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants 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.