ArticleBMC bioinformatics2018
The research on gene-disease association based on text-mining of PubMed.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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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
23 citing papers in PubMed.
- Consensus Molecules Associated with Parkinson's Disease.Neurology international · 2026Review
- Review
- Literature mining discerns latent disease-gene relationships.Bioinformatics (Oxford, England) · 2024Article
- Robustness evaluations of pathway activity inference methods on gene expression data.BMC bioinformatics · 2024Article
- Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.Journal of Taibah University Medical Sciences · 2023Article
- MantaID: a machine learning-based tool to automate the identification of biological database IDs.Database : the journal of biological databases and curation · 2023Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- Identification of core genes and pathways between geriatric multimorbidity and renal insufficiency: potential therapeutic agents discovered using bioinformatics analysis.BMC medical genomics · 2022Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Autoimmune Disease Classification Based on PubMed Text Mining.Journal of clinical medicine · 2022Article
- Text-Mining Approach to Identify Hub Genes of Cancer Metastasis and Potential Drug Repurposing to Target Them.Journal of clinical medicine · 2022Article
- Diseases 2.0: a weekly updated database of disease-gene associations from text mining and data integration.Database : the journal of biological databases and curation · 2022Article
- Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Towards a Consensus on Alzheimer's Disease Comorbidity?Journal of clinical medicine · 2021Review
- Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data.Entropy (Basel, Switzerland) · 2021Article
- RENET2: high-performance full-text gene-disease relation extraction with iterative training data expansion.NAR genomics and bioinformatics · 2021Article
- TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.Scientific reports · 2021Article
- Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis.Frontiers in cell and developmental biology · 2021Article
- Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundThe associations between genes and diseases are of critical significance in aspects of prevention, diagnosis and treatment. Although gene-disease relationships have been investigated extensively, much of the underpinnings of these associations are yet to be elucidated.
methodsA novel method integrates MeSH database, term weight (TW), and co-occurrence methods to predict gene-disease associations based on the cosine similarity between gene vectors and disease vectors. Vectors are transformed from the texts of documents in the PubMed database according to the appearance and location of the gene or disease terms. The disease related text data has been optimized during the process of constructing vectors.
resultsThe overall distribution of cosine similarity value was investigated. By using the gene-disease association data in OMIM database as golden standard, the performance of cosine similarity in predicting gene-disease linkage was evaluated. The effects of applying weight matrix, penalty weights for keywords (PWK), and normalization were also investigated. Finally, we demonstrated that our method outperforms heterogeneous network edge prediction (HNEP) in aspects of precision rate and recall rate.
conclusionsOur method proposed in this paper is easy to be conducted and the results can be integrated with other models to improve the overall performance of gene-disease association predictions.
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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.