Leveraging expression and network data for protein function prediction
| dc.contributor.author | Graim, Kiley, author | |
| dc.contributor.author | Ben-Hur, Asa, advisor | |
| dc.contributor.author | Anderson, Chuck, committee member | |
| dc.contributor.author | Achter, Jeff, committee member | |
| dc.date.accessioned | 2007-01-03T08:10:22Z | |
| dc.date.available | 2007-01-03T08:10:22Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Protein function prediction is one of the prominent problems in bioinformatics today. Protein annotation is slowly falling behind as more and more genomes are being sequenced. Experimental methods are expensive and time consuming, which leaves computational methods to fill the gap. While computational methods are still not accurate enough to be used without human supervision, this is the goal. The Gene Ontology (GO) is a collection of terms that are the standard for protein function annotations. Because of the structure of GO, protein function prediction is a hierarchical multi-label classification problem. The classification method used in this thesis is GOstruct, which performs structured predictions that take into account all GO terms. GOstruct has been shown to work well, but there are still improvements to be made. In this thesis, I work to improve predictions by building new kernels from the data that are used by GOstruct. To do this, I find key representations of the data that help define what kernels perform best on the variety of data types. I apply this methodology to function prediction in two model organisms, Saccharomyces cerevisiae and Mus musculus, and found better methods for interpreting the data. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Graim_colostate_0053N_11153.pdf | |
| dc.identifier | ETDF2012500159COMS | |
| dc.identifier.uri | http://hdl.handle.net/10217/67879 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | bioinformatics | |
| dc.subject | protein function prediction | |
| dc.title | Leveraging expression and network data for protein function prediction | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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