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Models Predicting Effects of Missense Mutations in Oncogenesis

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dc.contributor.advisor Vaisman, Iosif I.
dc.contributor.author Addepalli, KanakaDurga
dc.creator Addepalli, KanakaDurga
dc.date 2014-05
dc.date.accessioned 2014-09-23T19:01:02Z
dc.date.available 2015-05-27T04:13:50Z
dc.date.issued 2014-09-23
dc.identifier.uri https://hdl.handle.net/1920/8946
dc.description.abstract The recent avalanche in high-throughput genotyping, next generation sequencing technologies and re-sequencing of cancer genomes has revolutionized the field of cancer genomics. It has generated a humungous amount of mutational data and changed the way the cancer is being studied. Identification and characterization of these mutations and their mutational effect has become one of the major goals of cancer research. We present here a computational geometry approach based on the application of Delaunay tessellation derived four-body statistical potential function where the potentials are directly derived from the high-resolution protein x-ray crystallographic structures utilizing their atomic coordinates. Proteins and their mutants are characterized by potential topological scores and profiles, which measure the relative change in the overall sequence-structure compatibility. Residual scores and profiles are generated which quantify environmental perturbations from wild-type amino acids at every mutational position. We also present here an integrated database of human cancer missense mutations linked to their 3D structures, which has been created with the whole motivation of building a one stop shop of human missense mutations data sets huge and versatile enough to be used for training and testing of machine learning methodologies. With protein data from this database, we illustrate the use of potential topological cores and residual profiles in the prediction of mutational effects on protein structure and function and generating predictive models using machine-learning algorithms. We successfully apply supervised learning to training sets of protein mutants and generate models, which make statistically meaningful predictions of effects of missense mutations on cancer proteins.
dc.language.iso en_US en_US
dc.rights Copyright 2014 KanakaDurga Addepalli en_US
dc.subject cancer en_US
dc.subject database en_US
dc.subject Machine learning en_US
dc.subject missense mutations en_US
dc.title Models Predicting Effects of Missense Mutations in Oncogenesis en_US
dc.type Dissertation en
dc.description.note This work was embargoed by the author and will not be available until May 2015. en_US
thesis.degree.name PhD in Bioinformatics and Computational Biology en_US
thesis.degree.level Doctoral en
thesis.degree.discipline Bioinformatics and Computational Biology en
thesis.degree.grantor George Mason University en


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