Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization.

ABSTRACT: Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleterious and benign missense variants by means of a large number of features, including structure, sequence, interaction networks, gene disease associations as well as phenotypes. However, development of a reliable and accurate algorithm for merging heterogeneous information is highly needed as it could be captured all information of complex interactions on network that genes participate in. In this study we proposed a new method based on the non-negative matrix tri-factorization clustering method. We outlined two versions of the proposed method: t

SUBMITTER: Arani AA 

PROVIDER: S-EPMC8660898 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature