Systematic identification of latent disease-gene associations from PubMed articles.

ABSTRACT: Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LD

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC5786305 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature