Title

Authors

Affiliation

Faculty of Computer Science, Universitas Indonesia, Indonesia.

Email

ary.noviyanto@ui.ac.id; *Corresponding author

Article Type

Hypothesis

Date

Received January 19, 2012; Accepted January 20, 2012; Published February 03, 2012

TThe 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 the using of only microarray data are almost similar. The data integration strategy is not always better than using single data set in this case. The performance of classification absolutely depends on the features that are used to represent the object.

Keywords

Chickpea, Abiotic stress, Candidate genes, Sequence similarity

Citation

Noviyanto & Wasito, Bioinformation 8(3): 147-150 (2012)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.