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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Keat, M.C.W., Abdullah, R., Salam, R.A., Latif, A.A. (2004). Weightless Neural Network Array for Protein Classification. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_38
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DOI: https://doi.org/10.1007/978-3-540-30501-9_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24013-6
Online ISBN: 978-3-540-30501-9
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