Classification of Proteins via Successive State Splitting Algorithm of Hidden Markov Network
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概要
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<I>Hidden Markov Model</I> (HMM) introduces a stochastic approach to protein representation and motif abstraction. We need the stochastic classification which is seamless with HMM representation and abstraction. <I>Successive State Splitting</I> (SSS) classifies proteins represented by HMM. It uses no previous knowledge of the proteins. The SSS algorithm was originally developed for <I>allophone modeling</I>. It is based on continuous distribution of phenome data. It enables to obtain an appropriate <I>Hidden Markov Network</I> automatically, and HMM simultaneously. We map amino acids onto continuous space according to quantification based on PAM-250.
- 日本バイオインフォマティクス学会の論文
日本バイオインフォマティクス学会 | 論文
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