Machine learning and Hidden Markov Models application to transmembrane protein secondary structure prediction. Awarded as the best final-year Informatics project in Serbia.
Machine learning and Hidden Markov Models application to the transmembrane protein secondary structure prediction. Awarded as the best final-year Informatics project in Serbia.
The publication can be found here.
A Markov Chain is a system that expresses transitions from one state to another, with certain probabilities.
A Hidden Markov Model (HMM) is a Markov Chain in which the state sequence is unobservable. What could be observed is a sequence of outputs. Every produced output at a given moment depends only on the current state.
 |
 |
|---|---|
| Example of a Markov Chain | Example of a Hidden Markov Model |
A protein structure could be modeled as a HMM, where every amino acid is an output, and its position a state.
 |
|---|
| Example of a protein and its position, relative to a cell membrane |
Among others, the Baum-Welch algorithm is implemented for the unknown parameters estimation, while the Viterbi algorithm is implemented for finding the most likely sequence of hidden states.