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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mohamed, S. Rubin, D. Marwala, T. |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of the Witwatersrand, Johannesburg (Mohamed, S.; Rubin, D.; Marwala, T.) |
| Abstract | The problem of protein structural family classification remains a core problem in computational biology, with application of this technology applicable to problems in drug discovery programs and hypothetical protein annotation. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new information into their existing models. We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available. The fuzzy ARTMAP is found to be comparable to many of the widespread machine learning systems. The use of an evolutionary strategy in the selection and combination of individual classifiers into an ensemble system, coupled with the incremental learning ability of the fuzzy ARTMAP is proven to be suitable as a pattern classifier. The algorithm presented is tested using data from the G-Coupled Protein Receptors Database and shows good accuracy of 83%. |
| Starting Page | 19 |
| Ending Page | 24 |
| File Size | 1369370 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4370924 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Machine learning Fuzzy systems Learning systems Sequences Computational biology Pharmaceutical technology Drugs Neural networks Support vector machines |
| Content Type | Text |
| Resource Type | Article |
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