1616: Anuario de Literatura Comparada

Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., and Levine, A. J., 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences, USA, 96:6745–6750.
Balaguer, F., 2014. Cáncer colorrectal familiar y hereditario. Gastroenterología y Hepatología, 37:77–84. Berrar, D. P., Dubitzky, W., and Granzow, M., 2003. A Practical Approach to Microarray Data Analysis. Kluwer Academic Publishers, New York, Boston, Dordrecht, London, Moscow.
Bourne, P. E. and Wissig, H., 2003. Structural Bioinformatics. Wiley-Liss, Inc., Hoboken, New Jersey. Castellanos-Garzón, J. A. and Díaz, F., 2013. An Evolutionary Computational Model Applied to Cluster Analysis of DNA Microarray Data. Expert Systems with Applications, Elsevier, 40:2575–2591.
Castellanos-Garzón, J. A., García, C. A., Novais, P., and Díaz, F., 2013. A Visual Analytics Framework for Cluster Analysis of DNA Microarray Data. Expert Systems with Applications, Elsevier, 40:758–774.
Eisen, M., Spellman, T., Brown, P., and Botstein, D., 1998. Cluster Analysis and Display of Genome-wide Expression Patterns. Proceedings of the National Academy of Sciences, USA, 95:14863–14868.
Geoffrey, J. M., Do, K. A., and Ambroise, C., 2004. Analyzing Microarray Gene Expression Data. John Wiley & Sons, Inc., Hoboken, New Jersey.
Han, J. and Kamber, M., 2006. Data Mining: Concepts and Techniques. Elsevier Inc.
Haraldsdottir, S., Einarsdottir, H., Smaradottir, A., Gunnlaugsson, A., and Halfdanarson, T., 2014. Colorectal cancer-review. Laeknabladid, 2(100):75–82.
Inza, I., Larrañaga, P., Blanco, R., and Cerrolaza, A., 2004. Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence in Medicine, Elsevier, 31:91–103.
Jass, J., 2007. Classification of colorectal cancer based on correlation of clinical, morphological and molecular features. Histopathology, 50:: 223–230.
Jiang, D., Tang, C., and Zhang, A., 2004. Cluster Analysis for Gene Expression Data: A Survey. IEEE Transactions on Knowledge and Data Engineering, 16(11):1370–1386.
Kaufman, L. and Rousseeuw, P. J., 2005. Finding Groups in Data. An Introduction to Clustering Analysis. John Wiley & Sons, Inc., Hoboken, New Jersey.
Kim, S.-E., Paik, H., Yoon, H., Lee, J. E., Kim, N., and Sung, M.-K., 2015. Sex- and gender-specific disparities in colorectal cancer risk. World Journal of Gastroenterology: WJG, 17(21):5167–5175.
Lazar, C., Taminau, J., Meganck, D., S.and Steenhoff, Coletta, A., Molter, V., C.and deSchaetzen, Duque, H., R.and Bersini, and Nowé, A., 2012. A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis. IEEE/ACM Transactions On Computational Biology And Bioinformatics, 9(4):1106–1118.
Markowitz, S. and Bertagnolli, M., 2009. Molecular basis of colorectal cancer. New England Journal of Medicine, 25(361):2449–2460.
Martens, D., Baesens, B., Van, T. G., and Vanthienen, J., 2007. Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183:1466–1476.
Natarajan, A. and Ravi, T., 2014. A Survey on Gene feature selection using microarray data for cancer classification. International Journal of Computer Science & Communication (IJCSC), 5(1):126–129.
Perea, J., Lomas, M., and Hidalgo, M., 2011. Molecular basis of colorrectal cancer: towards an individualized management. Revista Española de Enfermedades Digestivas, 1(103):29–35.
Shraddha, S. and Anuradha, S., N.and Swapnil, 2014. Feature Selection Techniques and Microarray Data: A Survey. International Journal of Emerging Technology and Advanced Engineering, 4(1):179–183.
Tyagi, V. and Mishra, A., 2013. A Survey on Different Feature Selection Methods for Microarray Data Analysis. International Journal of Computer Applications, 67(16):36–40.
Vapnik, V., 1995. The nature of statistical learning theory. Springer, New York.
Wang, Y., Tetko, I., Hall, M., Frank, E., Facius, A., Mayer, K., and Mewes, H., 2005. Gene selection from microarray data for cancer classification - a machine learning approach. Computational Biology and Chemistry, Elsevier, 29:37–46.