Hidden Markov Model in Bioinformatics

An Overview

The Hidden Markov Model (HMM) is a relatively simple way to model sequential data. It implies that the Markov Model underlying the data is hidden or unknown to you. More specifically, you only know observational data and not information about the states.

What is a Markov Model?

A specific type of model that produces the data but you don’t know what processes are producing it. You basically use your knowledge to make an educated guess about the model’s structure.And that model structure is called Markov Model.

As I have tried in my videos to explain HMM in a more simpler way:

Part 1

Part 2

HMMs in Sequence Analysis

Determine the protein family for the given amino acid sequence.
Similar to fitting a speech signal to a word model.
Insertions, Deletions and substitutions are allowed.
In Computational Biology, a hidden Markov model (HMM) is a statistical approach that is frequently used for modelling biological sequences.

HMMs in Speech Recognition Problem

Determine the words spoken in a particular line framed.
They are adapted fit diverse classification problems for their robust statistical foundation, conceptual simplicity and malleability.
A good HMM accurately models the real world source of the observed real data and has the ability to simulate the source.

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