ArticlePLoS computational biology2010
Literature mining for the discovery of hidden connections between drugs, genes and diseases.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed.
- AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.Artificial intelligence in medicine · 2025Article
- A foundation model for human-AI collaboration in medical literature mining.Nature communications · 2025Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- Article
- The speed of information propagation in the scientific network distorts biomedical research.PeerJ · 2022Article
- Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.Frontiers in research metrics and analytics · 2021Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Gene Saturation: An Approach to Assess Exploration Stage of Gene Interaction Networks.Scientific reports · 2019Article
- Construction And Analysis Of The Time-Evolving Pain-Related Brain Network Using Literature Mining.Journal of pain research · 2019Article
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
- A context-based ABC model for literature-based discovery.PloS one · 2019Article
- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Computational drug repositioning using meta-path-based semantic network analysis.BMC systems biology · 2018Article
- The research on gene-disease association based on text-mining of PubMed.BMC bioinformatics · 2018Article
- Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.CPT: pharmacometrics & systems pharmacology · 2018Review
- Identifying Supplement Use Within Clinical Notes: An Applicationof Natural Language Processing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Rediscovering Don Swanson: the Past, Present and Future of Literature-Based Discovery.Journal of data and information science (Warsaw, Poland) · 2017Article
- Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec.BMC medical informatics and decision making · 2017Article
- Enriching plausible new hypothesis generation in PubMed.PloS one · 2017Article
- A crowdsourcing workflow for extracting chemical-induced disease relations from free text.Database : the journal of biological databases and curation · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.
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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.