Mining protein Regulatory Relationships Using Neural Network Methods for Early Prediction of SARS

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.

[1]  J. Sung,et al.  Serum Proteomic Fingerprints of Adult Patients with Severe Acute Respiratory Syndrome , 2006, Clinical chemistry.

[2]  Terence C W Poon,et al.  Serial Analysis of Plasma Proteomic Signatures in Pediatric Patients with Severe Acute Respiratory Syndrome and Correlation with Viral Load , 2004, Clinical chemistry.

[3]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[4]  Werner Dubitzky,et al.  Briefings in bioinformatics. , 2009, Briefings in bioinformatics.

[5]  De-Shuang Huang,et al.  Regulation probability method for gene selection , 2006, Pattern Recognit. Lett..

[6]  Martin D. Brand,et al.  Analysing microarray data using modular regulation analysis , 2004, Bioinform..

[7]  Zheng Wang,et al.  Protein Chip Array Profiling Analysis in Patients with Severe Acute Respiratory Syndrome Identified Serum Amyloid A Protein as a Biomarker Potentially Useful in Monitoring the Extent of Pneumonia , 2004, Clinical chemistry.

[8]  K. J. Ray Liu,et al.  Ensemble dependence model for classification and prediction of cancer and normal gene expression data , 2005, Bioinform..

[9]  J. Garland.  THE NEW ENGLAND JOURNAL OF MEDICINE , 1977, The Lancet.

[10]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[11]  L. Poon,et al.  Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia : a prospective study , 2003 .

[12]  Hong Yan,et al.  Dominant spectral component analysis for transcriptional regulations using microarray time-series data , 2004, Bioinform..

[13]  水谷 博之,et al.  SVM (Support Vector Machine) , 1999 .

[14]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[15]  Igor V. Tetko,et al.  Optimization models for cancer classification: extracting gene interaction information from microarray expression data , 2004, Bioinform..

[16]  Satoru Miyano,et al.  Inferring gene networks from time series microarray data using dynamic Bayesian networks , 2003, Briefings Bioinform..

[17]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[18]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.