Using deep learning to identify translational research in genomic medicine beyond bench to bedside.

ABSTRACT: Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database. Both classifiers employ salient features to determine the probability of curation-eligible publications, which can effectively reduce the workload of manual triage and curation process. We applied the C

SUBMITTER: Hsu YY 

PROVIDER: S-EPMC6367517 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature