ArticlePloS one2018
Automatic extraction of gene-disease associations from literature using joint ensemble learning.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction Extraction.Biomedicines · 2026Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Hybrid deep learning models for text-based identification of gene-disease associations.BioImpacts : BI · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
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- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.BMC bioinformatics · 2023Article
- Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks.International journal of molecular sciences · 2023Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- Article
- Predicting cross-tissue hormone-gene relations using balanced word embeddings.Bioinformatics (Oxford, England) · 2022Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- AnthraxKP: a knowledge graph-based, Anthrax Knowledge Portal mined from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- The potential role of procyanidin as a therapeutic agent against SARS-CoV-2: a text mining, molecular docking and molecular dynamics simulation approach.Journal of biomolecular structure & dynamics · 2022Article
- BioBERT and Similar Approaches for Relation Extraction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.Methods in molecular biology (Clifton, N.J.) · 2022Article
- TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.Scientific reports · 2021Article
- Article
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down. In this current scenario one of the crucial technologies is biomedical text mining, and relation extraction shows the promising result to explore the research of genes associated with diseases. By developing automatic extraction of gene-disease associations from the literature using joint ensemble learning we addressed this problem from a text mining perspective. In the proposed work, we employ a supervised machine learning approach in which a rich feature set covering conceptual, syntax and semantic properties jointly learned with word embedding are trained using ensemble support vector machine for extracting gene-disease relations from four gold standard corpora. Upon evaluating the machine learning approach shows promised results of 85.34%, 83.93%,87.39% and 85.57% of F-measure on EUADR, GAD, CoMAGC and PolySearch corpora respectively. We strongly believe that the presented novel approach combining rich syntax and semantic feature set with domain-specific word embedding through ensemble support vector machines evaluated on four gold standard corpora can act as a new baseline for future works in gene-disease relation extraction from literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.