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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Alvarez, M.A. Changhui Yan |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Computer Science, Utah State University, Logan, Utah, USA (Alvarez, M.A.) || Department of Computer Science, North Dakota State University, Fargo, North Dakota, USA (Changhui Yan) |
| Abstract | Computational methods play an important role in investigating the relationships between protein structure and function. In this study, we evaluate different graph representations of protein structures for kernel-based protein function prediction. We use shortest path graph kernels and support vector machines to predict whether a protein is an enzyme or not. We present three different and straightforward strategies for modeling protein structures. Accuracy averages for 10-fold cross-validation range from 84.31% to 86.97% for different modeling strategies, outperforming state-of-the-art work. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 461902 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424467662 |
| e-ISBN | 9781424467662 |
| DOI | 10.1109/CIBCB.2010.5510588 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-02 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Biochemistry Kernel Machine learning algorithms Support vector machines Learning systems Support vector machine classification Feature extraction Genomics Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
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