ArticleNucleic acids research2016
Beegle: from literature mining to disease-gene discovery.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.Methods in molecular biology (Clifton, N.J.) · 2022Article
- RNA Sequencing of CD4Journal of molecular neuroscience : MN · 2021Article
- Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs.Scientific reports · 2020Article
- Transcriptomics-Based Drug Repurposing Approach Identifies Novel Drugs against Sorafenib-Resistant Hepatocellular Carcinoma.Cancers · 2020Article
- Article
- Interpreting pathways to discover cancer driver genes with Moonlight.Nature communications · 2020Article
- Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs.Computational and structural biotechnology journal · 2020Article
- Article
- Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer's Disease: Insights From Data Mining and Enrichment Analysis.Frontiers in genetics · 2019Article
- Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature search) and carrying out preliminary experiments or screens (e.g. linkage or association studies, copy number analyses, expression profiling) to determine a set of promising candidates for experimental validation. This requires extensive time and monetary resources. We describe Beegle, an online search and discovery engine that attempts to simplify this process by automating the typical approaches. It starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes. Beegle is publicly available at http://beegle.esat.kuleuven.be/.
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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.