Computational Feature of Selection and Classification of RET Phenotypic Severity

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Publication Type pre-print
School or College School of Medicine
Department Biomedical Informatics
Creator Mitchell, Joyce A.
Other Author Crockett, David K.; Piccolo, Stephen R.; Narus, Scott P.; Facelli, Julio C.
Title Computational Feature of Selection and Classification of RET Phenotypic Severity
Date 2010-01-01
Description Although many reported mutations in the RET oncogene have been directly associated with hereditary thyroid carcinoma, other mutations are labelled as uncertain gene variants because they have not been clearly associated with a clinical phenotype. The process of determining the severity of a mutation is costly and time consuming. Informatics tools and methods may aid to bridge this genotype-phenotype gap. Towards this goal, machine-learning classification algorithms were evaluated for their ability to distinguish benign and pathogenic RET gene variants as characterized by differences in values of physicochemical properties of the residue present in the wild type and the one in the mutated sequence. Representative algorithms were chosen from different categories of machine learning classification techniques, including rules, bayes, and regression, nearest neighbour, support vector machines and trees. Machine-learning models were then compared to well-established techniques used for mutation severity prediction. Machine-learning classification can be used to accurately predict RET mutation status using primary sequence information only. Existing algorithms that are based on sequence homology (ortholog conservation) or protein structural data are not necessarily superior.
Type Text
Publisher OMICS Publishing Group
Volume 1
Issue 2
First Page 1
Last Page 4
Dissertation Institution University of Utah
Language eng
Bibliographic Citation Crockett, D. K., Piccolo, S. R., Narus, S. P., Mitchell, J. A. & Facelli, J. C. (2010). Computational Feature of Selection and Classification of RET Phenotypic Severity. Journal of Data Mining in Genomics & Proteinucs, 1(2), 1000103, 1-4.
Rights Management (c)OMICS Publishing Group
Format Medium application/pdf
Format Extent 2,027,638 bytes
Identifier uspace,17859
ARK ark:/87278/s6fb5mr6
Setname ir_uspace
Date Created 2013-02-15
Date Modified 2013-02-19
ID 708317
Reference URL https://collections.lib.utah.edu/ark:/87278/s6fb5mr6
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