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论文类型：期刊论文

发表时间：2015-08-01

发表刊物：CHEMICAL PHYSICS LETTERS

收录刊物：SCIE、EI、Scopus

卷号：634

页面范围：243-248

ISSN号：0009-2614

摘要：Identifying short linear motifs (SLiMs) usually suffers from lack of sufficient sequences. SLiMs with the same functional site class are typically characterized by similar motif patterns, which makes them hard to distinguish by generative motif discovery methods. A discriminative method based on maximal mutual information estimation (MMIE) of hidden Markov models (HMMs) is proposed. It masks ordered regions to improve signal to noise ratio and augments the training set to diminish the impact of the lack of sequences. Experimental results on a dataset selected from the Eukaryotic Linear Motif (ELM) resource show that the proposed method is effective and practical. (C) 2015 Elsevier B.V. All rights reserved.