Similarity-driven multi-view embeddings from high-dimensional biomedical data.

ABSTRACT: Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction datas

SUBMITTER: Avants BB 

PROVIDER: S-EPMC8009088 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature