Bioinformatics analyses of alternative splicing, est-based and machine learning-based prediction
dc.contributor.author | Xia, Jing | |
dc.date.accessioned | 2008-12-22T14:36:12Z | |
dc.date.available | 2008-12-22T14:36:12Z | |
dc.date.graduationmonth | December | en |
dc.date.issued | 2008-12-22T14:36:12Z | |
dc.date.published | 2008 | en |
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.description.advisor | William H. Hsu | en |
dc.description.degree | Master of Science | en |
dc.description.department | Department of Computing and Information Sciences | en |
dc.description.level | Masters | en |
dc.identifier.uri | http://hdl.handle.net/2097/1113 | |
dc.language.iso | en_US | en |
dc.publisher | Kansas State University | en |
dc.subject | support vector machine | en |
dc.subject | alternative splicing | en |
dc.subject.umi | Computer Science (0984) | en |
dc.title | Bioinformatics analyses of alternative splicing, est-based and machine learning-based prediction | en |
dc.type | Thesis | en |