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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]

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

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