Automatic extraction of gene-disease associations from literature using joint ensemble learning

• DOI: 10.1371/journal.pone.0200699
• Publication Date: 2018-07-26T18:09:30Z
• Authors: Balu Bhasuran; Jeyakumar Natarajan
• Keywords: Relationship extraction; Word embedding; Biomedical text mining; Ensemble Learning; Feature (linguistics); Named Entity Recognition; Feature Engineering

A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging drug-targetable pathways biomarker discovery. However, time cost manual curation heavily slows it down. In this current scenario one the crucial technologies text mining, relation extraction shows promising result explore with diseases. By developing automatic gene-disease using joint ensemble learning we addressed problem a mining perspective. proposed work, employ supervised machine approach which rich feature set covering conceptual, syntax semantic properties jointly learned word embedding are trained vector for extracting four gold standard corpora. Upon evaluating promised results 85.34%, 83.93%,87.39% 85.57% F-measure on EUADR, GAD, CoMAGC PolySearch corpora respectively. We strongly believe that presented novel combining domain-specific through machines evaluated act as new baseline future works