ArticleScientific reports2017
An analysis of disease-gene relationship from Medline abstracts by DigSee.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 papers, 1 of them a synthesis that pooled it.
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
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Endometriosis Pathophysiologic-Related Genes Based on Meta-Analysis and Bayesian Approach.International journal of molecular sciences · 2025Pooled it
- Novel Alzheimer's disease-associated variants and genetic interactions identified from UK biobank whole-exome sequencing data using IBI-DT.Scientific reports · 2026Article
- Integrated pharmacoanalysis, bioinformatics analysis, and experimental validation to identify the ingredients and mechanisms of Xiao-Luo-Wan in uterine fibroids treatment.Pharmaceutical biology · 2025Article
- Human genomic regions of systemic interindividual epigenetic variation are implicated in neurodevelopmental and metabolic disorders.medRxiv : the preprint server for health sciences · 2025Article
- 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
- Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.European journal of medical research · 2024Article
- Literature mining discerns latent disease-gene relationships.Bioinformatics (Oxford, England) · 2024Article
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- Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis.Biomedicines · 2021Article
- TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.Scientific reports · 2021Article
- Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model.Scientific reports · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer's disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.
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What OpenQuestion holds
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.