Publication Type:

Conference Paper

Source:

2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012, Orlando, FL, p.564-566 (2012)

ISBN:

9781450316705

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84869426188&partnerID=40&md5=f12305a1c4e770d1d5d6b8f22a72e369

Keywords:

Bayes factor, bioinformatics, Candidate genes, Classification (of information), Corpus callosum, Data points, Decision boundary, Diseases, Feature extraction, Feature-based, gene expression, Gene expression analysis, Gene expression profiling, Gene ontology, Genetic Variation, genomics, Handicapped persons, Heritable disorders, High-throughput, Mental retardation, Molecular mechanism, Neurological disorders, Neuropsychiatric disorder, Prioritization, statistical significance, Support vector machines, Training sets

Abstract:

The progress in understanding of molecular mechanisms underlying common heritable disorders (e.g. autism, schizophrenia, diabetes) depends on the availability of new bioinformatics approaches for identification of their characteristic genetic variations and associated multidimensional patterns of inheritance. High-throughput genome-wide studies (e.g. sequencing, gene expression profiling) result in hundreds of potential candidate genes. Prioritizing these genes and finding the best candidates contributing to a disease phenotype is one of the most important problems of genomics. We present an approach for prioritization of disease candidate genes using Support Vector Machine (SVM) and ontology associations. Features are extracted from both hierarchical and non-hierarchical ontology space (e.g user defined customized ontologies, Gene Ontology(GO) ). We select a subset of features according to enrichment scores in a training set of genes and use these to train a classifier using SVM. Ranking of the genes in the query set (e.g. the results of gene expression analysis) is based on a distance from the decision boundary to data points. Results obtained using the proposed approach to the analysis of several neurological disorders (autism, mental retardation, and agenesis of corpus callosum) are presented.

Notes:

cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@151f7dd3 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@467db27a Through org.apache.xalan.xsltc.dom.DOMAdapter@66508bbd; Conference Code:93833

Cite this Research Publication

B. Xie, Sulakhe, D., Dr. Bhadrachalam Chitturi, Agam, G., Maltsev, N., and Gilliam, T. C., “Prediction of candidate genes for neuropsychiatric disorders using feature-based enrichment”, in 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012, Orlando, FL, 2012, pp. 564-566.

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