Classification of heterogeneous microarray data by maximum entropy kernel

Background: There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets. Results: We introduce a new type of kernel called maximum entropy (ME) kernel, which has no pre-defined function but is generated by kernel entropy maximization with sample distance matrices as constraints, into the field of SVM classification of microarray data. We assessed the performance of the ME kernel with three different data: heterogeneous kidney carcinoma, noiseintroduced leukemia, and heterogeneous oral cavity c...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Wataru Fujibuchi, Tsuyoshi Kato
• Keywords: BMCBI 2007, Data Kernel Family, Microarray Data, Vectorial Data