ReviewF1000Research2017
Recent advances in predicting gene-disease associations.
Review in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- A global survey of systems biology-based predictions of gene-rare disease associations to enhance new diagnoses.Scientific reports · 2026Article
- KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.Frontiers in bioinformatics · 2025Article
- Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases.Bioinformatics (Oxford, England) · 2024Article
- Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information.NAR genomics and bioinformatics · 2024Article
- Identifying Candidate Gene-Disease Associations via Graph Neural Networks.Entropy (Basel, Switzerland) · 2023Article
- NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification.Bioinformatics (Oxford, England) · 2023Article
- Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer.Journal, genetic engineering & biotechnology · 2023Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Article
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations.BMC bioinformatics · 2021Article
- Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.Frontiers in genetics · 2021Article
- GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network.Frontiers in genetics · 2020Article
- Cross-disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma.Oncology letters · 2019Article
- Analysis of disease organ as a novel phenotype towards disease genetics understanding.Journal of biomedical informatics · 2019Article
- The Alzheimer's comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction.JAMIA open · 2019Article
- Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data.BMC bioinformatics · 2018Article
- Identifying genotype-phenotype relationships in biomedical text.Journal of biomedical semantics · 2017Article
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
No grant is acknowledged in the PubMed record.
Abstract
Deciphering gene-disease association is a crucial step in designing therapeutic strategies against diseases. There are experimental methods for identifying gene-disease associations, such as genome-wide association studies and linkage analysis, but these can be expensive and time consuming. As a result, various
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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.