Artificial immune system for classification of cancer

Authors:
Shin Ando;Hitoshi Iba
Affiliations:
Dept. of Electronics Engineering, School of Engineering, University of Tokyo;Dept. of Frontier Informatics, School of Frontier Science, University of Tokyo
Venue:
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Year:
2003

Citing 5
Cited 1

Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Class prediction and discovery using gene expression data

RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Class discovery in gene expression data

RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Artficial Immune Systems and Their Applications

Artficial Immune Systems and Their Applications
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning

Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning

Genetic Programming and Evolvable Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method for cancer type classification based on microarray-monitored data. The method is based on artificial immune system(AIS), which utilizes immunological recognition for classification. The system evolutionarily selects important genes; optimize their weights to derive classification rules. This system was applied to gene expression data of acute leukemia patients to classify their cancer class. The primary result found few classification rules which correctly classified all the test samples and gave some interesting implications for feature selection principles.