Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes.

Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes. Pac Symp Biocomput. 2020;25:427-438 Authors: Park S, Kar N, Cheong JH, Hwang TH Abstract Accurate identification of pathways associated with cancer phenotypes (e.g., cancer subtypes and treatment outcomes) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix tri-factorization (NMTF) can be successfully applied to identify pathways associated with specific cancer types or disease classes as a prognostic and predictive biomarker. However, one key limitation of non-negative factorization methods, including various non-negative bi-factorization methods, is their limited ability to handle negative input data. For example, many types of molecular data that consist of real-values containing both positive and negative values (e.g., normalized/log transformed gene expression data where negative values represent down-regulated expression of genes) are not suitable input for these algorithms. In addition, most previous methods provide just a single point estimate and hence cannot deal with uncertainty effectively.To address these limitations, we propose a Bayesian semi-nonnegative matrix trifactorization method to identify pathways associated with cancer phenotypes from a realvalued input matrix, e.g...
Source: Pacific Symposium on Biocomputing - Category: Bioinformatics Tags: Pac Symp Biocomput Source Type: research