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bioRxiv · 10.1101/415950

Multiple-kernel learning for genomic data mining and prediction

Wilson, C.·Wang, X.·Li, K.·Kuan, P. F.
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Abstract

Advances in medical technology have allowed for customized prognosis, diagnosis, and personalized treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for integration of multiple high throughput data sources, however, there are currently no implementations of MKL in R. In this paper, we give some background material for support vector machine (SVM) and introduce an R package, RMKL, which provides R and C++ code to implement several MKL algorithms for classification and regression problems. The provided implementations of MKL are compared using benchmark data and TCGA ovarian cancer. We demonstrate that combining multiple data sources can lead to a better classification scheme than simply using a single data source.

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Wilson, C., Wang, X., Li, K., Kuan, P. F.. 2018-09-13. Multiple-kernel learning for genomic data mining and prediction. https://doi.org/10.1101/415950

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