Evaluation of data integration strategies based on kernel method of clinical and microarray data.

ABSTRACT: The cancer classification problem is one of the most challenging problems in bioinformatics. The data provided by Netherland Cancer Institute consists of 295 breast cancer patient; 101 patients are with distant metastases and 194 patients are without distant metastases. Combination of features sets based on kernel method to classify the patient who are with or without distant metastases will be investigated. The single data set will be compared with three data integration strategies and also weighted data integration strategies based on kernel method. Least Square Support Vector Machine (LS-SVM) is chosen as the classifier because it can handle very high dimensional features, for instance, microarray data. The experiment result shows that the performance of weighted late integration and th

SUBMITTER: Noviyanto A 

PROVIDER: S-EPMC3283887 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature