DNA microarray experiments generate thousands of gene expression measurement simultaneously. Analyzing the difference of gene expression in cell and tissue samples is useful in diagnosis of disease. This paper presents an Artificial Immune System for classifying microarray-monitored data. The system evolutionarily selects important features and optimizes their weights to derive classification rules. This system was applied to two datasets of cancerous cells and tissues. The primary result found few classification rules which correctly classified all the test samples and gave some interesting implications for feature selection.
|Number of pages||12|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 1 Dec 2003|