Combining intrinsic disorder prediction and augmented training of hidden Markov models improves discriminative motif discovery

Abstract

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.