Page 49 - Biosystems Engineering

30 Cha pte r O n e

Sokhansanj, B. A., Fitch, J. P., Quong, J. N., and Quong, A. A. 2004. Linear fuzzy
gene network models obtained from microarray data by exhaustive search.
BMC Bioinformatics 5:108.
Somogyi, R. 1999. Making sense of gene-expression data. Trends in Biotechnology,
17 (Supp.1):17–24.
Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B.,
Brown, P. O., et al. 1998. Comprehensive identification of cell cycle-regu-
lated genes of the yeast Saccharomyces cerevisiae by microarray hybridiza-
tion. Molecular Biology of the Cell 9:3273–3297.
Su, M. and Chang, H. 2001. A new model of self-organizing neural networks
and its application in data projection. IEEE Transactions on Neural Networks
12:153–158.
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E.,
Lander, E. S., and Golub, T. R. 1999. Interpreting patterns of gene expres-
sion with self-organizing maps: Methods and application to hematopoi-
etic differentiation. Proceedings of the National Academy of Science, USA 96:
2907–2912.
Tibshirani, R., Walther, G., and Hastie, T. 2000. Estimating the number of clus-
ters in a dataset via the gap statistic. Technical Report 208, Department of
Statistics. Stanford, CA: Stanford University.
Tomida, S., Hanai, T., Honda, H., and Kobayashi, T. 2002. Analysis of expres-
sion profile using fuzzy adaptive resonance theory. Bioinformatics 18(8):
1073–1083.
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R.,
Botstein, D., et al. 2001. Missing value estimation methods for DNA microar-
rays. Bioinformatics 17:520–525.
Ushizawa, K., Herath, C. B., Kaneyama, K., Shiojima, S., Hirasawa, A., Takahashi,
T., Imai, K., et al. 2004. cDNA microarray analysis of bovine embryo gene
expression profiles during the pre-implantation period. Reproductive Biology
and Endocrinology 2:77.
Wang, Y., Lu J., Lee, R., Gu, Z., and Clarke, R. 2002. Iterative normalization
of cDNA microarray data. IEEE Transactions on Information Technology in
Biomedicine 6(1):29–37.
Weaver, D. C., Workman, C. T., and Stormo, G. D. 1999. Modeling regulatory net-
works with weight matrices. Pacific Symposium on Biocomputing 3:112–123.
Wingender, E., Chen, X., Fricke, E., Geffers, R., Hehl, R., Liebich, I., Krull, M.,
et al. 2001. The TRANSFAC system on gene expression regulation. Nucleic
Acids Research 29(1):281–283.
Woolf, P. J. and Wang, Y. 2000. A fuzzy logic approach to analyzing gene expres-
sion data. Physiological Genomics 3:9–15.
Xie, X. L. and Beni, G. 1991. A validity measure for fuzzy clustering. IEEE
Transactions on Pattern Analysis and Machine Intelligence 13(8):841–847.
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E., and Ruzzo, W. L. 2001a.
Model-based clustering and data transformations for gene expression data.
Bioinformatics 17:977–987.
Yeung, K. Y., Haynor, D. R., and Ruzzo, W. L. 2001b. Validating clustering for
gene expression data. Bioinformatics 17:309–318.
Zak, E. D., Doyle, F. J. III, Gonye, G. E., and Schwaber, J. S. 2001. Simulation
studies for the identification of genetic networks from cDNA array and
regulatory activity data. Proceedings of the Second International Conference
on Systems Biology California Institute of Technology, Pasadena, CA, USA.
231–238.
Zhu, J. and Zhang, M. Q. 1999. SCPD: A promoter database of the yeast
Saccharomyces cerevisiae. Bioinformatics 15:607–611.
Zhu, Z., Pilpel, Y., and Church, G. M. 2002. Computational identification of
transcription factor binding sites via a transcription-factor-centric clustering
(TFCC) algorithm. Journal of Molecular Biology 318:71–81.