04/01/2000 09:00 AM
Biomolecular Engineering
DNA microarray experiments generating thousands of gene expression measurements are being used to gather information from tissue and cell samples about gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyze this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97,802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyze a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analyzed. The results are comparable to those previously obtained. We show that other machine learning methods perform comparably to the SVM on many of those datasets as well.
