ArticleBMC bioinformatics2005
Speeding disease gene discovery by sequence based candidate prioritization.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 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
100 citing papers in PubMed.
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.BMC medical informatics and decision making · 2021Article
- Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning.European journal of human genetics : EJHG · 2021Article
- TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain.BMC bioinformatics · 2021Article
- Disease gene prediction with privileged information and heteroscedastic dropout.Bioinformatics (Oxford, England) · 2021Article
- Identification of Dysregulated Genes for Late-Onset Alzheimer's Disease Using Gene Expression Data in Brain.Journal of Alzheimer's disease & Parkinsonism · 2020Article
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.Nature communications · 2019Article
- A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.PloS one · 2019Article
- Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles.BioMed research international · 2019Article
- Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.BMC neurology · 2018Article
- Review of Drug Repositioning Approaches and Resources.International journal of biological sciences · 2018Review
- Mimvec: a deep learning approach for analyzing the human phenome.BMC systems biology · 2017Article
- Constructing an integrated gene similarity network for the identification of disease genes.Journal of biomedical semantics · 2017Article
- Analysis of shared homozygosity regions in Saudi siblings with attention deficit hyperactivity disorder.Psychiatric genetics · 2017Article
- Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information.BMC genomics · 2016Article
- Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.Briefings in bioinformatics · 2016Review
- Functional and Genomic Features of Human Genes Mutated in Neuropsychiatric Disorders.The open neurology journal · 2016Article
- MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.EURASIP journal on bioinformatics & systems biology · 2015Article
- Disease gene prioritization using network and feature.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRegions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning.
resultsWe examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time.
conclusionPROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies.
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.