Name
Abstract | ||
|---|---|---|
Proteins are classified into families based on structural or functional similarities. Artificial intelligence methods such as Hidden Markov Models, Neural Networks and Fuzzy Logic have been used individually in the field of bioinformatics for tasks such as protein classification and microarray data analysis. We integrate these three methods into a protein classification system for the purpose of drug target identification. Through integration, the strengths of each method can be harnessed as one, and their weaknesses compensated. Artificial intelligence methods are more flexible than traditional multiple alignment methods, and hence, offers greater problem-solving potential. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-32251-1_9 | LSGRID |
Keywords | Field | DocType |
hybrid framework,hidden markov models,artificial intelligence method,neural networks,protein classification,fuzzy logic,greater problem-solving potential,functional similarity,parallel artificial intelligence,microarray data analysis,protein classification system,drug target identification,hidden markov model,neural network,multiple alignment,artificial intelligent,drug targeting | Neuro-fuzzy,Computational intelligence,Computer science,Fuzzy logic,Drug target,Message Passing Interface,Artificial intelligence,Artificial neural network,Hidden Markov model,Multiple sequence alignment,Machine learning | Conference |
Volume | ISSN | ISBN |
3370 | 0302-9743 | 3-540-25208-8 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Chew Wooi Keat | 1 | 4 | 1.17 |
| Rosni Abdullah | 2 | 156 | 24.82 |
| Rosalina Abdul Salam | 3 | 70 | 10.83 |