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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Okada, K. Flores, L. Wong, M. Petkovic, D. |
| Copyright Year | 2014 |
| Description | Author affiliation: Center for Comput. for Life Sci., San Francisco State Univ., San Francisco, CA, USA (Wong, M.) || Comput. Sci. Dept., San Francisco State Univ. San Francisco, San Francisco, CA, USA (Okada, K.; Flores, L.; Petkovic, D.) |
| Abstract | Machine learning-based prediction of protein functions plays a key role in bioinformatics and pharmaceutical research, facilitating swift discovery of new drugs in high-throughput settings. This paper presents an adaptation of Random Forest to the structure-based protein function prediction. Our system represents protein's 3D physicochemical structural information in microenvironment descriptors whose spatial resolution is much finer than other sequence-based protein descriptors. We prepare our datasets for seven active sites from five protein function classes by using multiple public data banks and train Random Forest classifiers to identify these seven function models in proteins. This paper presents two experiment studies: 1) a 5-fold stratified cross-validation for comparing Random Forest with Naive Bayes and Support Vector Machine and 2) systematic comparison of Random Forest's two variable importance measures. Promising results of these studies demonstrate a potential for Random Forest to improve the accuracy of the current protein function assays. |
| Starting Page | 3138 |
| Ending Page | 3143 |
| File Size | 215861 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.541 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Radio frequency Proteins Niobium Support vector machines Computational modeling Training Accuracy |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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