Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Hidden markov model ppt by Shivangi Saxena 2791 views
- Lecture 7: Hidden Markov Models (HMMs) by Marina Santini 3824 views
- Hidden Markov Models by phvu 6367 views
- Hidden markov model by Haitham Ahmed 952 views
- HIDDEN MARKOV MODEL AND ITS APPLICA... by Muzzammil Abdulra... 1823 views
- Data Science - Part XIII - Hidden M... by Derek Kane 3022 views

3,095 views

2,729 views

2,729 views

Published on

No Downloads

Total views

3,095

On SlideShare

0

From Embeds

0

Number of Embeds

5

Shares

0

Downloads

264

Comments

0

Likes

5

No embeds

No notes for slide

- 1. INTRODUCTION OF HIDDEN MARKOV MODEL Mohan Kumar Yadav M.Sc Bioinformatics JNU JAIPUR
- 2. HIDDEN MARKOV MODEL(HMM) Real-world has structures and processes which have observable outputs. – Usually sequential . – Cannot see the event producing the output. Problem: how to construct a model of the structure or process given only observations.
- 3. HISTORY OF HMM • Basic theory developed and published in 1960s and 70s • No widespread understanding and application until late 80s • Why? – Theory published in mathematic journals which were not widely read. – Insufficient tutorial material for readers to understand and apply concepts.
- 4. Andrei Andreyevich Markov 1856-1922 Andrey Andreyevich Markov was a Russian mathematician. He is best known for his work on stochastic processes. A primary subject of his research later became known as Markov chains and Markov processes .
- 5. HIDDEN MARKOV MODEL • A Hidden Markov Model (HMM) is a statical model in which the system is being modeled is assumed to be a Markov process with hidden states. • Markov chain property: probability of each subsequent state depends only on what was the previous state.
- 6. EXAMPLE OF HMM • Coin toss: – Heads, tails sequence with 2 coins – You are in a room, with a wall – Person behind wall flips coin, tells result – Coin selection and toss is hidden – Cannot observe events, only output (heads, tails) from events – Problem is then to build a model to explain observed sequence of heads and tails.
- 7. EXAMPLE OF HMM • Weather – Once each day weather is observed – State 1: rain – State 2: cloudy – State 3: sunny – What is the probability the weather for the next 7 days will be: – sun, sun, rain, rain, sun, cloudy, sun – Each state corresponds to a physical observable event
- 8. HMM COMPONENTS • A set of states (x’s) • A set of possible output symbols (y’s) • A state transition matrix (a’s) – probability of making transition from one state to the next • Output emission matrix (b’s) – probability of a emitting/observing a symbol at a particular state • Initial probability vector – probability of starting at a particular state – Not shown, sometimes assumed to be 1
- 9. EXAMPLE OF HMM 0.3 0.7 Rain Dry 0.2 • Two states : ‘Rain’ and ‘Dry’. • Transition probabilities: P(‘Rain’|‘Rain’)=0.3 , P(‘Dry’|‘Rain’)=0.7 , P(‘Ra’)=0.6 . • in’|‘Dry’)=0.2, P(‘Dry’|‘Dry’)=0.8 • Initial probabilities: say P(‘Rain’)=0.4 , P(‘Dry 0.8
- 10. CALCULATION OF HMM
- 11. HMM COMPONENTS
- 12. COMMON HMM TYPES • Ergodic (fully connected): – Every state of model can be reached in a single step from every other state of the model. • Bakis (left-right): – As time increases, states proceed from left to right
- 13. HMM IN BIOINFORMATICS • Hidden Markov Models (HMMs) are a probabilistic model for modeling and representing biological sequences. • They allow us to do things like find genes, do sequence alignments and find regulatory elements such as promoters in a principled manner.
- 14. PROBLEMS OF HMM • Three problems must be solved for HMMs to be useful in real-world applications ● 1) Evaluation ● 2) Decoding ● 3) Learning
- 15. EVOLUTION OF PROBLEM Given a set of HMMs, which is the one most likely to have produced the observation sequence? GACGAAACCCTGTCTCTATTTATCC p(HMM-3)? p(HMM-1)? p(HMM-2)? HMM 1 HMM 2 HMM 3 p(HMM-n)? … HMM n
- 16. DECODING PROBLEM
- 17. TRAINING PROBLEM From raw seqence data… AATAGAGAGGTTCGACTCTGCAT TTCCCAAATACGTAATGCTTACGG TACACGACCCAAGCTCTCTGCTT GAATCCCAAATCTGAGCGGACAG ATGAGGGGGCGCAGAGGAAAAA CAGGTTTTGGACCCTACATAAAN AGAGAGGTTCGTAAATAGAGAGG TTCGACTCTGCATTTCCCAAATAC GTAATGCTTACGGTTAAATAGAGA GGTTCGACTCTGCATTTCCCAAA TACGTAATGCTTACGGTACACGA CCCAAGCTCTCTGCTTGTAACTT GTTTTNGTCGCAGCTGGTCTTGC CTTTGCTGGGGCTGCTGAC to Transition Probabilities A+ C+ A+ H o w ? C+ G+ T+ ACGT- 0.17 0.16 0.15 0.07 0.01 0.01 0.01 0.01 0.26 0.36 0.33 0.35 0.01 0.01 0.01 0.01 G+ T+ 0.42 0.26 0.37 0.37 0.01 0.01 0.01 0.01 0.11 0.18 0.11 0.17 0.01 0.01 0.01 0.01 A- C- G- T- 0.01 0.01 0.01 0.01 0.29 0.31 0.24 0.17 0.01 0.01 0.01 0.01 0.2 0.29 0.23 0.23 0.01 0.01 0.01 0.01 0.27 0.07 0.29 0.28 0.01 0.01 0.01 0.01 0.2 0.29 0.2 0.28
- 18. HMM-APPLICATION • DNA Sequence analysis • Protein family profiling • Predprediction • Splicing signals prediction • Prediction of genes • Horizontal gene transfer • Radiation hybrid mapping, linkage analysis • Prediction of DNA functional sites. • CpG island
- 19. HMM-APPLICATION • Speech Recognition • Vehicle Trajectory Projection • Gesture Learning for Human-Robot Interface • Positron Emission Tomography (PET) • Optical Signal Detection • Digital Communications • Music Analysis
- 20. HMM-BASED TOOLS • GENSCAN (Burge 1997) • FGENESH (Solovyev 1997) • HMMgene (Krogh 1997) • GENIE (Kulp 1996) • GENMARK (Borodovsky & McIninch 1993) • VEIL (Henderson, Salzberg, & Fasman 1997)
- 21. BIOINFORMATICS RESOURCES • PROBE www.ncbi.nlm.nih.gov/ • BLOCKS www.blocks.fhcrc.org/ • META-MEME www.cse.ucsd.edu/users/bgrundy/metameme.1.0.html • SAM www.cse.ucsc.edu/research/compbio/sam.html • HMMERS hmmer.wustl.edu/ • HMMpro www.netid.com/ • GENEWISE www.sanger.ac.uk/Software/Wise2/ • PSI-BLAST www.ncbi.nlm.nih.gov/BLAST/newblast.html • PFAM www.sanger.ac.uk/Pfam/
- 22. Refrences • Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2), 257-285. • Essential bioinformatics, Jin Xion • http://www.sociable1.com/v/Andrey-Markov108362562522144#sthash.tbdud7my.dpuf
- 23. Thank You!

No public clipboards found for this slide

Be the first to comment