Name
Abstract | ||
|---|---|---|
Proteins are classified into superfamilies based on structural or functional similarities. Neural networks have been used before to abstract the properties of protein superfamilies. One approach is to use a single conventional neural network to abstract the properties of different protein superfamilies. Since the number of protein superfamilies is in the thousands, we propose another approach – one network attuned to one protein superfamily. Furthermore, we propose to use weightless neural networks, coupled with Hidden Markov Models (HMM). The advantages of weightless neural networks are: (a) the ability to learn with only one presentation of training patterns – thus improving performance, (b) ease of implementation, and (c) ease of parallelization – thus improving scalability. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30501-9_38 | PDCAT |
Keywords | Field | DocType |
hidden markov models,protein classification,neural network,weightless neural network,protein superfamily,weightless neural network array,training pattern,different protein superfamily,functional similarity,single conventional neural network,hidden markov model | Similitude,Markov model,Computer science,Protein superfamily,Weightless neural networks,Artificial intelligence,Weightless,Artificial neural network,Hidden Markov model,Machine learning,Scalability | Conference |
Volume | ISSN | ISBN |
3320 | 0302-9743 | 3-540-24013-6 |
Citations | PageRank | References |
4 | 0.49 | 2 |
Authors | ||
4 | ||
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 |
| Aishah Abdul Latif | 4 | 4 | 0.49 |