Directed acyclic graph kernels for structural RNA analysis |
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| Authors: | Kengo Sato Toutai Mituyama Kiyoshi Asai Yasubumi Sakakibara |
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| Institution: | (1) Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku Tokyo, 135-8073, Japan;(2) Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku Tokyo, 135-0064, Japan;(3) Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama Kanagawa, 223-8522, Japan;(4) Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa Chiba, 277-8561, Japan |
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| Abstract: | Background Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers.
In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of
string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However,
applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity. |
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