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- Title
MINING PROTEIN REGULATORY RELATIONSHIPS USING NEURAL NETWORK METHODS FOR EARLY PREDICTION OF SARS.
- Authors
WANG, HONG-QIANG; ZHU, HAI LONG; ZHENG, CHUNHOU; YIP, TIMOTHY T. C.; CHO, WILLIAM C. S.; LAW, STEPHEN C. K.
- Abstract
This paper proposes to model protein regulation networks associated with severe acute respiratory syndrome (SARS) for early prediction of SARS. In the approach, specific to a patient group, a regulatory network is simulated using a fully-connected neural network and is optimized towards minimizing a novel energy function that is defined as a measure of disagreement between the input and output of the network. The nonlinear version of the network is achieved by applying a sigmoid function. Experimental results show that the proposed approaches can capture regulatory patterns associated with SARS and efficiently implement early prediction of SARS.
- Subjects
SARS disease; CORONAVIRUSES; SIGMOIDOSCOPY; ARTIFICIAL neural networks; COMPUTER algorithms; BIOMARKERS
- Publication
Journal of Circuits, Systems & Computers, 2009, Vol 18, Issue 8, p1397
- ISSN
0218-1266
- Publication type
Article
- DOI
10.1142/S0218126609005745