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.
Highlights
Hidden Markov Model (HMM) is a general modeling technique suited to represent a sequence of hidden features in time or space, in which each hidden feature causes or emits an observation [1]
We will show the elements of an HMM and a set of practical rules to model in an orderly manner the variables of a situation using HMMs, and answer, through the most basic HMM algorithms Forward and Viterbi, respectively, 2 questions: (1) What is the probability of an observed sequence given a model? and (2) What is the most probable sequence of hidden features in it? we will interpret the results in an analysis of the parameters under the light of the modeled problem
When modeling an HMM, the hidden variables are the ones that represent the elements of the answer we are looking for
Summary
OPEN ACCESS Citation: Fuentes-Beals C, Valdes-Jimenez A, Riadi G (2022) Hidden Markov Modeling with HMMTeacher. AIsUit :pPoslesaibselecotnofilermartnhaatanldlhceraedainteglaevfierlsstaHreirdedpernesMenaterkdocovrMreoctdlye:l (HMM) without programming skills or understanding the algorithms in detail? 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. 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. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.
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