Manolis Kellis
@manoliskellis1·24.9K subscribers·255 videos
MIT CompBio Lecture 04 - HMMs I
Posted
October 3, 2018
Views
3,767
Likes
32
Comments
1
Engagement
0.88%
October 3, 2018
3,767
32
1
0.88%
Every word spoken in this episode is indexed. Type any phrase to jump straight to the moment it was said.
Type any word or phrase that may have been spoken. Click a result to seek the player to that exact moment.
Try a name, a topic, or a quoted line
See what was published immediately before and after this episode.
1:13:32Now PlayingMIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
Fall 2018
Lecture 04 - HMMs I - Modeling Biological Sequences using Hidden Markov Models
1. Modeling sequential data
- Recognize a type of sequence, genomic, oral, verbal, visual, etc…
2. Definitions
- Markov Chains
- Hidden Markov Models (HMMs)
3. Examples of HMMs
- Recognizing GC-rich regions, preferentially-conserved elements, coding exons, protein-coding gene structures, chromatin states
4. Our first computations
- Running the model: know model, generate sequence of a type
- Evaluation: know model, emissions, states, infer p
- Viterbi: know model, emissions, find optimal path
- Forward: know model, emissions, infer total p over all paths
5. Next time:
- Posterior decoding
- Supervised learning
- Unsupervised learning: Baum-Welch, Viterbi training
Slides for Lecture 4:
Sentinel Indexing in Progress
Metadata and chapters are available. Claim extraction for this episode is pending.
All video content is delivered via YouTube embedded players in accordance with the YouTube Terms of Service. Sentinel provides research tools that promote discovery and accountability across political media.
