Abstract
Statistical analyses on DNA microarray expression data have made it possible to extract information from tissue and cell samples. Recently, Support Vector Machines (SVMs)[3], one of the supervised learning methods, have been employed to classify gene expression data and have shown greater performance than other learning methods, especially in the case where the number of features is larger than the number of samples[1]. However, the characteristics of SVMs are not clear. Thus, in this work, we will study how the number of features and regulatable parameters on SVMs affect performance of classification on DNA microarray. Gene expression profiles derive from various types of cancers using Affymetrix GeneChip, including our institute sample data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
