ADVANTAGES AND

DISADVANTAGES OF HIDDEN MARKOV

MODEL

By:P.Joshna Rani16031d7902

HIDDEN MARKOV MODEL: HMM is called hidden because only the symbols emitted by

the system are observable, not the under lying random walk

between states.An HMM can be visualized as a finite state machine. it

generates a protein sequence by emitting amino acids as it

progresses through a series of states.

ADVANTAGES: Strong statistical foundation

Efficient learning algorithms-learning can take place directly from raw sequence data.

Allow consistent treatment of insertion and deletion penalties

in the form of locally learnable

Can handle inputs of variable length-they are the most

flexible generalization of sequence profiles.

Wide variety of applications including multiple alignment,

data mining and classification, structural analysis, and pattern

discovery.

Can be combined into libraries.

DISADVANTAGES: HMMs often have a large number of unstructured parameters.

First order HMMs are limited by their first-order markov property

They cannot express dependencies between hidden states.

Proteins fold into complex 3-D shapes determining their function.

The HMM is unable to capture higher order correlation among

amino acids in a protein molecule.

Only a small fraction of distributions over the space of possible

sequences can be represented by a reasonably constrained HMM.

APPLICATIONS:

Identification of G-protein coupled receptors

Clustering of paths for a subgroup

Gene prediction

Modeling protein domains

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