Journal Article

A new algorithm to train hidden Markov models for biological sequences with partial labels

Li, Jiefu, Liao, Li, Lee, Jung-Youn

2021

Overview

Background Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when sequences are labelled or by expectation maximization, such as the Baum–Welch algorithm, when sequences are unlabelled. However, increasingly there are situations where sequences are just partially labelled. In this paper, we designed a new training method based on the Baum–Welch algorithm to train HMMs for situations in which only partial labeling is available for certain biological problems. Results Compared with a similar method previously reported that is designed for the purpose of active learning in text mining, our method achieves significant improvements in model training, as demonstrated by higher accuracy when the trained models are tested for decoding with both synthetic data and real data. Conclusions A novel training method is developed to improve the training of hidden Markov models by utilizing partial labelled data. The method will impact on detecting de novo motifs and signals in biological sequence data. In particular, the method will be deployed in active learning mode to the ongoing research in detecting plasmodesmata targeting signals and assess the performance with validations from wet-lab experiments.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Active learning / Algorithms / Bioinformatics / Biological effects / Biological models (mathematics) / Biological sequences / Biomedical and Life Sciences / Computational Biology / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Constrained Baum-Welch algorithm / Data mining / Expected values / Experiments / Hidden Markov model / Hidden Markov models / Labeling / Labels / Learning / Life Sciences / Machine learning / Markov Chains / Methodology / Methodology Article / Methods / Microarrays / Model accuracy / Model testing / Partial label / Performance assessment / Plasmodesmata / Protein families / Protein research / Proteins - genetics / Sequence analysis / Training