Hidden Markov Modeling with HMMTeacher

Abstract

Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user’s manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.

LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network

“…To help wet lab researchers identify novel antimicrobial peptides, a variety of computational methods for antimicrobial peptide identification have been proposed. Many algorithms combine machine learning or statistical analysis techniques such as discriminant analysis (DA) ( Kouw and Loog, 2021 ; Beck and Sharon, 2022 ), fuzzy K-nearest neighbors ( Zhai et al, 2020 ), hidden Markov models ( Fuentes-Beals et al, 2022 ), logistic regression ( Fagerland and Hosmer, 2012 ), random forests (RF) ( Ziegler and Koenig, 2014 ), and support vector machines (SVM) ( Azar and El-Said, 2014 ). Although these models have made great progress in antimicrobial peptide recognition, the following challenges still exist: First, many related classification tasks based on machine learning suffer from the small number of samples.…”