ArticlePLoS computational biology2013
Chapter 15: disease gene prioritization.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- In silico SNP prediction of selected protein orthologues in insect models for Alzheimer's, Parkinson's, and Huntington's diseases.Scientific reports · 2023Article
- DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.PLoS computational biology · 2023Article
- Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction.Genes · 2021Article
- A graph centrality-based approach for candidate gene prediction for type 1 diabetes.Immunologic research · 2021Article
- Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning.European journal of human genetics : EJHG · 2021Article
- Computational Methods for Prediction of Human Protein-Phenotype Associations: A Review.Phenomics (Cham, Switzerland) · 2021Review
- A Microfluidic System for Stable and Continuous EEG Monitoring from Multiple Larval Zebrafish.Sensors (Basel, Switzerland) · 2020Article
- Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.BMC genomics · 2019Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- Integrating molecular networks with genetic variant interpretation for precision medicine.Wiley interdisciplinary reviews. Systems biology and medicine · 2019Review
- MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization.BMC bioinformatics · 2018Article
- Article
- A large-scale benchmark of gene prioritization methods.Scientific reports · 2017Article
- The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges.Frontiers in veterinary science · 2017Review
- Data-Driven Approach To Determine Popular Proteins for Targeted Proteomics Translation of Six Organ Systems.Journal of proteome research · 2016Article
- Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.PLoS computational biology · 2016Article
- Protein function in precision medicine: deep understanding with machine learning.FEBS letters · 2016Review
- An integer programming framework for inferring disease complexes from network data.Bioinformatics (Oxford, England) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.
Indexed as
Identifiers
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.