Prediction of colon cancer using an evolutionary neural network

Colon cancer is second only to lung cancer as a cause of cancer-related mortality in Western countries. Colon cancer is a genetic disease, propagated by the acquisition of somatic alterations that influence gene expression. DNA microarray technology provides a format for the simultaneous measurement of the expression level of thousands of genes in a single hybridization assay. The most exciting result of microarray technology has been the demonstration that patterns of gene expression can distinguish between tumors of different anatomical origin. Standard statistical methodologies in classification and prediction do not work well or even at all when N (a number of samples)

[1]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[2]  D A Rew,et al.  DNA microarray technology in cancer research. , 2001, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[3]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[4]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[5]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[6]  Seung-Ik Lee,et al.  Exploiting diversity of neural ensembles with speciated evolution , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[9]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[10]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[11]  Yi Li,et al.  Bayesian automatic relevance determination algorithms for classifying gene expression data. , 2002, Bioinformatics.

[12]  J Carl Barrett,et al.  Microarrays : the use of oligonucleotides and cDNA for the analysis of gene expression , 2003 .

[13]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[14]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[15]  Nir Friedman,et al.  Tissue classification with gene expression profiles. , 2000 .

[16]  M. Conrad,et al.  Computation: evolutionary, neural, molecular , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.

[17]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[18]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[19]  Sung-Bae Cho,et al.  Self-Organizing Map with Dynamical Node Splitting: Application to Handwritten Digit Recognition , 1997, Neural Computation.

[20]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[21]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[22]  David Corne,et al.  Predicting alarms in supermarket refrigeration systems using evolved neural networks and evolved rulesets , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  G. Getz,et al.  Coupled two-way clustering analysis of gene microarray data. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[24]  D. Kerr,et al.  Gene therapy strategies for colon cancer. , 2000, Molecular medicine today.

[25]  Dunja Mladenic,et al.  Feature selection on hierarchy of web documents , 2003, Decis. Support Syst..

[26]  Danh V. Nguyen,et al.  Tumor classification by partial least squares using microarray gene expression data , 2002, Bioinform..

[27]  H. Frierson,et al.  Classifying human cancer by analysis of gene expression. , 2003, Trends in molecular medicine.

[28]  Ivo Grosse,et al.  Gene selection criterion for discriminant microarray data analysis based on extreme value distributions , 2003, RECOMB '03.

[29]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[30]  Joon-Hyun Ahn,et al.  Speciated neural networks evolved with fitness sharing technique , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[31]  S. P. Fodor,et al.  High density synthetic oligonucleotide arrays , 1999, Nature Genetics.

[32]  David B. Fogel,et al.  Verifying Anaconda's expert rating by competing against Chinook: experiments in co-evolving a neural checkers player , 2002, Neurocomputing.

[33]  E. Boerwinkle,et al.  Feature (gene) selection in gene expression-based tumor classification. , 2001, Molecular genetics and metabolism.

[34]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[35]  C. Rosenow,et al.  Monitoring gene expression using DNA microarrays. , 2000, Current opinion in microbiology.

[36]  N. Friedman,et al.  Tissue Classi cation with Gene Expression Pro les , 2004 .

[37]  Roded Sharan,et al.  Algorithmic approaches to clustering gene expression data , 2001 .

[38]  Momiao Xiong,et al.  Tclass: tumor classification system based on gene expression profile , 2002, Bioinform..

[39]  Indra Neil Sarkar,et al.  Characteristic attributes in cancer microarrays , 2002, Journal of Biomedical Informatics.

[40]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[41]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[42]  WestonJason,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002 .