Discovery of microRNA-mRNA modules via population-based probabilistic learning, J.-G. Joung, K.-B. Hwang, J.-W. Nam, S.-J. Kim and B.-T. Zhang, Bioinformatics, 23(9):1141-1147, 2007. [PDF]
Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation, J.-G. Joung, D. Shin, R. H. Seong, and B.-T. Zhang, Bioinformatics, 22(16):2005-2011, 2006.[PDF]
Bayesian Network Classifiers for Gene Expression Analysis, Zhang, B.-T. and Hwang, K.-B., A Practical Approach to Microarray
Data Analysis, chapter 8, pp. 150-165, Kluwer Academic Publishers, 2003.
Gene Expression Pattern Analysis via Latent Variable Models
Coupled with Topographic Clustering, Chang, J.-H., Chi, S. W., and
Zhang, B.-T., Genomics & Informatics, vol. 1, no. 1, pp.
32-39, 2003. [PDF]
Self-Organizing Latent Lattice Models for Temporal Gene
Expression Profiling, Zhang, B.-T, Yang, J. and Chi, S. W.
Machine Learning, vol. 52, no. 1/2, pp. 67-89, 2003. [PDF]
Analysis of Gene Expression Profiles and Drug Activity Patterns
by Clustering and Bayesian Network Learning, Chang, J.-H., Hwang,
K.-B., and Zhang, B.-T., Methods of Microarray Data Analysis II,
chapter 11, pp. 169-184, Kluwer Academic Publishers, 2002. [PDF]
Applying Machine Learning Techniques to Analysis of Gene
Expression Data: Cancer Diagnosis, Hwang, K.-B., Cho, D.-Y., Park,
S.-W., Kim, S.-D., and Zhang, B.-T., Methods of Microarray Data
Analysis, chapter 12, pp. 167-182, Kluwer Academic Publishers, 2002.
[PDF]
Evolutionary multiobjective optimization for DNA sequence design, S.-Y.
Shin, I.-H. Lee, B.-T. Zhang, Multi-Objective Optimization in Computational
Intelligence: Theory and Practice, (to be published) [-]
Identification of Novel Anti-angiogenic Factors by in Silico
Functional Gene Screening Method, Lee, S.-K., Choi, Y. S., Cha, J.,
Moon, E.-J., Lee, S.-W., Bae, M.-K., Sohn, T.-K., Won, Y., Ma, S., Kong,
E. B., Lee, H., Lim, S., Chang, D., Kim, Y.-J., Kim, C. W., Zhang,
B.-T., Kim, K.-W, Journal of Biotechnology, vol. 105, no. 1-2,
pp. 51-60, 2003. [PDF]
PIE: An online prediction system for protein-protein interactions from text, S. Kim, S.-Y. Shin, I.-H. Lee, S.-J. Kim, R. Sriram, and B.-T. Zhang, Nucleic Acids Research, 35:W411-W415, 2008. [PDF]
Identifying protein-protein interaction sentences using boosting and kernel methods, S.-Y. Shin, S. Kim, J.-H. Eom, B.-T. Zhang, and R. Sriram, Second BioCreative Challenge Workshop, pp. 187-192, 2007. [PDF]
A tree kernel-based method for protein-protein interaction mining from biomedical literature, Jae-Hong Eom, Sun Kim, Seong-Hwan Kim, and Byoung-Tak Zhang, Lecture Notes in Computer Science, KDLL 2006, 3886:42-52, 2006. [PDF]
Mining protein interaction from biomedical literature with relation kernel method, Jae-Hong Eom and Byoung-Tak Zhang, Lecture Notes in Computer Science, ISNN 2006, 3973:642-647, 2006. [PDF]
Classification of Human Papillpmavirus (HPV) Risk Type via Text Mining, Park, S.-B., Hwang, S., and Zhang, B.-T., Genomics &
Informatics, vol. 1, no. 2, pp. 80-86, 2003. [PDF]
Mining the Risk Types of Human Papillomavirus (HPV) by
AdaCost, Park, S.-B., S. Hwang, and Zhang, B.-T., Lecture Notes
in Computer Science, vol. 2736, pp. 403-412, 2003. [PDF]
Classification of the Risk Types of Human Papillpmavirus by
Decision Trees, Park, S.-B., S. Hwang, and Zhang, B.-T., Lecture
Notes in Computer Science, vol. 2690, pp. 540-544, 2003. [PDF]
Mining the Risk Types of Human Papillomavirus (HPV) by
Cost-Sensitive Learning, Hwang, S., Park, S.-B., and Zhang, B.-T.,
Proceedings of PAKDD2003 Workshop on Biological Data Mining, pp.
107-118, 2003.
Information Extraction with Hidden Markov Models, Eom, J.-H.
and Zhang, B.-T., The First Joint Conference on Text in Biology:
Biological Research with Information Extraction & Open-Access
Publications (BRIE & OAP), 2001.
This page is maintained by Byoung-Hee Kim
Last update: July 1, 2008.