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.