Page 48 - Biosystems Engineering
P. 48

Micr oarray Data Analysis Using Machine Learning Methods       29

               Liang, S., Fuhrman, S., and Somogyi, R. 1998. REVEAL, a general reverse engineer-
                  ing algorithm for inference of genetic network architectures. Pacific Symposium
                  on Biocomputing 3:18–29.
               Maass, W. 1995. Vapnik-Chervonenkis dimension of neural networks. In:  The
                  Handbook of Brain Theory and Neural Networks, ed. M. A. Arbib, 522 26. Cambridge,
                  MA: MIT Press.
               Moody, J. 1992. The effective number of parameters: An analysis of generaliza-
                  tion and regularization in nonlinear learning systems. In: Advances in Neural
                  Information Processing Systems, eds. J. Moody, S. J. Hanson, and R. P. Lippmann,
                  847–854. San Mateo, CA: Morgan Kaufmann.
               Pal, N. R. and Bezdek, J. C. 1995. On cluster validity for the fuzzy c-means model.
                  IEEE Transactions on Fuzzy Systems 3(3):370–379.
               Pe’er, D., Regev, A., Elidan, G., and Friedman, N. 2001. Inferring subnetworks from
                  perturbed expression profiles. Bioinformatics 17:Suppl.:S215–S224.
               Pomeroy, S. L., Tamayo, P., Gaasenbeek, M., Sturla, L. M., Angelo, M., McLaughlin,
                  M. E., Kim J. Y. H., et al. 2002. Prediction of central nervous system embryonal
                  tumour outcome based on gene expression. Nature (Lond.) 415:436–442.
               Qian, J., Lin, J., Luscombe, N. M., Yu, H., and Gerstein, M. 2003. Prediction of regula-
                  tory networks: genomewide identification of transcription factor targets from
                  gene expression data. Bioinformatics 19(15):1917–1926.
               Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C. H., Angelo, M.,
                  Ladd, C., et al. 2001. Multiclass cancer diagnosis using tumor gene expression
                  signatures. Proceedings of the National Academy of Science, USA 98:15149–1554.
               Ransohoff, D. F. 2005. Bias as a threat to the validity of cancer molecular-marker
                  research. Nature Reviews Cancer 5:142–149.
               Ressom, H., Reynolds, R., and Varghese, R .S. 2003a. Increasing the efficiency
                  of fuzzy logic-based gene expression data analysis. Physiological Genomics
                  13:107–117.
               Ressom, H., Wang D., and Natarajan P. 2003b. Clustering gene expression data
                  using adaptive double self-organizing map. Physiological Genomics 14:35–46.
               Rezaee, M. R., Lelieveldt, B. P. F., and Reiber, J. H. C. 1998. A new cluster validity
                  index for the fuzzy c-means. Pattern Recognition Letters 18:237–246.
               Rodriguez-Zas, S. L., Band, M. R., Everts, R. E., Southey, B. R., Liu, Z. L., and Lewin,
                  H. A. 2003. Analysis of gene expression patterns in the cattle digestive system.
                  Journal of Dairy Science 86 (Suppl. 1):628.
               Roulet, E., Fisch, I., Junier, T., Bucher, P., and Mermod, N. 1998. Evaluation of com-
                  puter tools for the prediction of transcription factor binding sites on genomic
                  DNA. In Silico Biology 1:21–28.
               Rumelhart, D. E. and McClelland, J. L, eds. 1986. Parallel distributed processing:
                  Explorations in the microstructure of cognition. Foundations, vol. 1.  Cambridge,
                  MA: MIT Press.
               Schmitt, W. A. Jr., Raab, R. M., and Stephanopoulos, G. 2004. Elucidation of gene
                  interaction networks through time-lagged correlation analysis of transcrip-
                  tional data. Genome Research 14(8):1654–1663.
               Schuldiner, O., Yanover, C., and Benvenisty, N. 1998. Computer analysis of the entire
                  budding yeast genome for putative targets of the GCN4 transcription factor.
                  Currents in Genetics 33:16–20.
               Shin, A. and Iba, H. 2003. Construction of genetic network using evolutionary algo-
                  rithm and combined fitness function, Genome Informatics 14:94–103.
               Shipp, M. A., Ross, K. N, Tamayo, P., Weng A. P., Kutok, J. L., Aguiar, R. C.,
                  Gaasenbeek, M., et al. 2002. Diffuse large B-cell lymphoma outcome predic-
                  tion by gene expression profiling and supervised machine learning. Nature
                  Medicine 8:68–74.
               Shmulevich, I., Dougherty, E. R., Kim, S., and Zhang, W. 2002. Probabilistic Boolean
                  networks: a rule-based uncertainty model for gene regulatory networks.
                  Bioinformatics 18(2):261–274.
               Smith, V. A., Jarvis, E. D., and Hartemink, A. J. 2002. Evaluating functional network
                  inference using simulations of complex biological systems. Bioinformatics 18:
                  S216–S224.
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