Computational recognition of protein-coding genes using multiple genomic alignments
Abstract/Contents
- Abstract
- In this thesis, I describe three main contributions I have made toward creating more accurate systems for the computational recognition of protein-coding genes. First, I present N-SCAN, a gene predictor based on a hidden Markov model that uses Bayesian networks to model multiple alignments. I also describe CONTRAST, a discriminative gene predictor based on a conditional random field and a set of support vector machines for recognizing coding region boundaries. Both N-SCAN and CONTRAST represented substantial improvements over the state-of-the-art at the time they were introduced. Additionally, I give an algorithm for training conditional random fields that maximizes an approximation to labelwise accuracy, as opposed to the usual maximum likelihood approach. This algorithm proved key to CONTRAST's success.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Gross, Samuel Solomon |
|---|---|
| Associated with | Stanford University, Computer Science Department |
| Primary advisor | Batzoglou, Serafim |
| Thesis advisor | Batzoglou, Serafim |
| Thesis advisor | Ng, Andrew Y, 1976- |
| Thesis advisor | Sidow, Arend |
| Advisor | Ng, Andrew Y, 1976- |
| Advisor | Sidow, Arend |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Samuel Gross. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Ph. D. Stanford University 2010 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2010 by Samuel Solomon Gross
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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