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This document introduces machine learning approaches for protein structure prediction. It discusses using machine learning to predict a protein's structure given its sequence by looking at regions of the protein and learning to classify them. Two main questions are addressed: how to describe protein structures and how to train predictors on examples. Common machine learning techniques for this problem include decision trees, neural networks, and logic programs. The importance of testing predictors on unseen data to avoid overfitting is also covered.

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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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