Lecture 9 slides: Machine learning for Protein Structure ...

Introduction to Bioinformatics 9. Machine Learning for Protein Structure Prediction #1 Course 341 Department of Computing Imperial College, London © Simon Colton

Remember the Scenario ,[object Object],[object Object],[object Object],[object Object],[object Object]

The Database Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

There is another way… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

A Good Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

For example ,[object Object], Alpha Alpha Alpha Alpha Inter Inter Beta Beta Beta Beta Trained Predictor Alpha Helix Beta Sheet Further Processing

Two Main Questions ,[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Learning in a Nutshell ,[object Object],Predictor out ,[object Object],[object Object],[object Object],[object Object]

Machine Learning Considerations ,[object Object],[object Object],[object Object],[object Object],[object Object]

Types of Learning Problems in Bioinformatics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Types of Representations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Advantages of Representations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Decision Tree Representations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Artificial Neural Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Logic Program Representations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Artificial Neural Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Logic Programs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Testing Learned Predictors #1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

N-Fold Cross Validation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Testing Learned Predictors #2 ,[object Object],[object Object],[object Object],[object Object],Predicted F Predicted T number = a number = b (false pos) number = c (false neg) number = d Actually F Actually T ,[object Object],[object Object],[object Object],[object Object],[object Object]

Comparing Learning Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Overfitting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Lecture 9 slides: Machine learning for Protein Structure ...

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