SynthesisBriefings in bioinformatics2019
Computational resources associating diseases with genotypes, phenotypes and exposures.
Synthesis in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations.Interdisciplinary sciences, computational life sciences · 2026Article
- Identification of Potential Therapeutic Targets on the Level of DNA/mRNAs, Proteins and Metabolites: A Systematic Mapping Review of Scientific Texts' Fragments from Open Targets.Current issues in molecular biology · 2023Review
- A novel liver cancer diagnosis method based on patient similarity network and DenseGCN.Scientific reports · 2022Article
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization.Frontiers in microbiology · 2022Article
- Knowledge bases and software support for variant interpretation in precision oncology.Briefings in bioinformatics · 2021Article
- Open Targets Platform: supporting systematic drug-target identification and prioritisation.Nucleic acids research · 2021Article
- CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects.Oncoimmunology · 2021Article
- ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs.RNA biology · 2020Article
- ncPro-ML: An integrated computational tool for identifying non-coding RNA promoters in multiple species.Computational and structural biotechnology journal · 2020Article
- Decoding the Role of Sphingosine-1-Phosphate in Asthma and Other Respiratory System Diseases Using Next Generation Knowledge Discovery Platforms Coupled With Luminex Multiple Analyte Profiling Technology.Frontiers in cell and developmental biology · 2020Article
- Public data sources to support systems toxicology applications.Current opinion in toxicology · 2019Article
- Open Targets Platform: new developments and updates two years on.Nucleic acids research · 2019Article
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 causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.
Indexed as
Identifiers
What Socratic 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 Socratic graph.