Evaluation of input data modality choices on functional gene embeddings.

ABSTRACT: 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 de

SUBMITTER: Brechtmann F 

PROVIDER: S-EPMC10629286 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature