Bioinformatics analyses of alternative splicing, est-based and machine learning-based prediction

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dc.contributor.author Xia, Jing
dc.date.accessioned 2008-12-22T14:36:12Z
dc.date.available 2008-12-22T14:36:12Z
dc.date.issued 2008-12-22T14:36:12Z
dc.identifier.uri http://hdl.handle.net/2097/1113
dc.description.abstract Alternative splicing is a mechanism for generating different gene transcripts (called iso- forms) from the same genomic sequence. Finding alternative splicing events experimentally is both expensive and time consuming. Computational methods in general, and EST analy- sis and machine learning algorithms in particular, can be used to complement experimental methods in the process of identifying alternative splicing events. In this thesis, I first iden- tify alternative splicing exons by analyzing EST-genome alignment. Next, I explore the predictive power of a rich set of features that have been experimentally shown to affect al- ternative splicing. I use these features to build support vector machine (SVM) classifiers for distinguishing between alternatively spliced exons and constitutive exons. My results show that simple, linear SVM classifiers built from a rich set of features give results comparable to those of more sophisticated SVM classifiers that use more basic sequence features. Finally, I use feature selection methods to identify computationally the most informative features for the prediction problem considered. en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject support vector machine en
dc.subject alternative splicing en
dc.title Bioinformatics analyses of alternative splicing, est-based and machine learning-based prediction en
dc.type Thesis en
dc.description.degree Master of Science en
dc.description.level Masters en
dc.description.department Department of Computing and Information Sciences en
dc.description.advisor William H. Hsu en
dc.subject.umi Computer Science (0984) en
dc.date.published 2008 en
dc.date.graduationmonth December en


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