1. SMA5422: Special Topics in Biotechnology
Lecture 8: Machine learning techniques in sequence analysis
Introduction
Methods
Application examples for sequence analysis

2. Introduction to Machine Learning
Goal:
To “improve” (gaining knowledge, enhancing computing capability)
Tasks:
Forming concepts by data generalization.
Compiling knowledge into compact form
Finding useful explanations for valid concepts.
Clustering data into classes.
Reference:
Machine Learning in Molecular Biology Sequence Analysis .
Internet links:

3. Introduction to Machine Learning
Category:
Inductive learning.
Forming concepts from data without a lot of knowledge from domain (learning from examples).
Analytic learning.
Use of existing knowledge to derive new useful concepts (explanation based learning).
Connectionist learning.
Use of artificial neural networks in searching for or representing of concepts.
Genetic algorithms.
To search for the most effective concept by means of Darwin’s “survival of the fittest” approach.

4. Machine Learning Methods
Inductive learning:
Concept learning and example-based learning
Concept
learning:

5. Machine Learning Methods
Analytic
learning:

6. Machine Learning Methods
Neural network:

7. Machine Learning Methods
Genetic algorithms:

8. Machine Learning in Sequence Analysis
Example:
Protein secondary structure prediction:
Procedure:
Amino acids classified according to chemical property
Each amino acid in a sequence is represented by a set of descriptors
Rules are generated based on positive and negative examples.
The learned rules:
Descriptors 1, Descriptors 2, …, Descriptors n -> Secondary Structure Type
Tiny or Polar, Large, Aromatic or M, Large and Non-negative -> Helix
Accuracy achieved: 60%
Progress in Machine Learning: Proc. 2nd European
Working Session in Learning. Page

9. Homework
Read the references about machine learning given in the lecture.
Read at least one of the following references about SVM in biology:
Bioinformatics 16, (2000);
17, (2001);
17, (2001)