Journal of Data Mining in Genomics & Proteomics

David K. Crockett

Publications

• Research Article
  Computational Feature Selection and Classification of RET Phenotypic Severity
  Author(s): David K. Crockett, Stephen R. Piccolo, Scott P. Narus, Joyce A. Mitchell and Julio C. FacelliDavid K. Crockett, Stephen R. Piccolo, Scott P. Narus, Joyce A. Mitchell and Julio C. Facelli
  
  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 machi.. View More»
  DOI: 10.4172/2153-0602.1000103

  Abstract PDF