ArticleDNA research : an international journal for rapid publication of reports on genes and genomes2009
Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach.
Article in DNA research : an international journal for rapid publication of reports on genes and genomes, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Artificial intelligence and the hunt for immunological disorders.Current opinion in allergy and clinical immunology · 2020Review
- Article
- Simulation of the Dynamics of Primary Immunodeficiencies in B Cells.Frontiers in immunology · 2018Article
- Article
- A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis.Biology direct · 2011Article
- Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology.Molecular biotechnology · 2011Article
- svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.
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