Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature.

ABSTRACT:

Objective

Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine.

Materials and methods

We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about

SUBMITTER: Singhal A 

PROVIDER: S-EPMC4926749 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature