Machine Learning Techniques for bioinformatics
Course
In Carshalton
Description
-
Type
Course
-
Location
Carshalton
-
Duration
5 Days
After completing this course attendees should have a good knowledge of machine learning techniques as they apply to Bioinformatics. Suitable for: Attendees are expected to have a good working knowledge of statistics, calculus and algebra as might be gained from completing a science degree with a substantial mathematical component.
Facilities
Location
Start date
Start date
Reviews
Course programme
After a brief overview of the underlying molecular biology concepts, and of basic probability theory and frameworks for probabilisting reasoning and modeling the course goes on to survey the various machine learning algorithms that have been developed over the last 50 years. Specific algorithms and techniques such as Neural Networks, Hiddent Markov Models, Probabilistic Graphical Models and Stochastic grammars are then explored in greater detail The course is predominantly a taught course. For those who are willing to put in the extra hours there will be opportunities to do some lab and project work.
Course Benefits
After completing this course attendees should have a good knowledge of machine learning techniques as they apply to Bioinformatics.
Course Contents
Foundations - Molecular Biology
- Digital symbol sequences for representing biological data
- Overview of genomes, proteins and proteomes
- Information content of biological sequences
- Basics of Bayesian modeling
- Bayesian inference and induction
- Probabilistic modeling and inference
- Dynamic programming
- Gradient descent
- Simulated annealing
- EM (Expectation Maximisation) and GEM (Generalised Expectation Maximisation) algorithms
- Markov-Chain and Monte-Carlo methods
- Evolutionary and Genetic Algorithms
- Learning algorithms
- Neural networks
- Protein secondary structure prediction
- Prediction of signal peptides and their cleavage sites
- Eukaryotic gene finding and Intron Splice Site prediction
- Learning algorithms based on HMMs
- Applications of HMMs to proteins - structure and function
- Applications of HMMs to DNA and RNA - structure and function
- Markov Models and DNA symmetries
- Markov Models and gene location
- Bidirectional recurrent neural networks for protein secondary structure prediction
Machine Learning Techniques for bioinformatics