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 alg

SUBMITTER: Fuentes-Beals C 

PROVIDER: S-EPMC8830650 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature