ReviewJournal of integrative bioinformatics2017
Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.
Review in Journal of integrative bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 39 citations in OpenAlex.
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
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- Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.International journal of molecular sciences · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Decoding bee cleptoparasitism through comparative transcriptomics of Coelioxoides waltheriae and its host Tetrapedia diversipes.Scientific reports · 2024Article
- Genome-wide functional association networks: background, data & state-of-the-art resources.Briefings in bioinformatics · 2020Review
- Article
- Gene Regulatory Networks ofFrontiers in microbiology · 2020Article
- Global Role of Crop Genomics in the Face of Climate Change.Frontiers in plant science · 2020Review
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- From markers to genome-based breeding in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Review
- Data Integration in Poplar: 'Omics Layers and Integration Strategies.Frontiers in genetics · 2019Review
- Bottlenecks for genome-edited crops on the road from lab to farm.Genome biology · 2018Article
- Turning omics data into therapeutic insights.Current opinion in pharmacology · 2018Review
- Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach.Journal of integrative bioinformatics · 2018Article
- Computational miRNomics - Integrative Approaches.Journal of integrative bioinformatics · 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 at 1 institution in 1 country.
Funding
Abstract
Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.
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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.