Peter Bajcsy, PhDAutomated Learning GroupNational Center for Supercomputing ApplicationsUniversity of Illinoispbajcsy@ncsa.uiuc.edu

January 31, 2002

Data Mining in Bioinformatics

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Outline

• Introduction

• Overview of Microarray Problem

• Image Analysis

• Data Mining

• Validation

• Summary

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Introduction: Recommended Literature

1. Bioinformatics – The Machine Learning Approach by P. Baldi & S. Brunak, 2nd edition, The MIT Press, 2001

2. Data Mining – Concepts and Techniques by J. Han & M. Kamber, Morgan Kaufmann Publishers, 2001

3. Pattern Classification by R. Duda, P. Hart and D. Stork, 2nd edition, John Wiley & Sons, 2001

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Introduction: Microarray Problem in Bioinformatics Domain

• Problems in Bioinformatics Domain—Data production at the levels of molecules,

cells, organs, organisms, populations—Integration of structure and function data,

gene expression data, pathway data, phenotypic and clinical data, …

—Prediction of Molecular Function and Structure

—Computational biology: synthesis (simulations) and analysis (machine learning)

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Microarray Problem: Major Objective

• Major Objective: Discover a comprehensive theory of life’s organization at the molecular level—The major actors of molecular biology: the

nucleic acids, DeoxyriboNucleic acid (DNA) and RiboNucleic Acids (RNA)

—The central dogma of molecular biology

Proteins are very complicated molecules with 20 different amino acids.

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Input and Output of Microarray Data Analysis

• Input: Laser image scans (data) and underlying experiment hypotheses or experiment designs (prior knowledge)

• Output: —Conclusions about the input hypotheses or knowledge

about statistical behavior of measurements—The theory of biological systems learnt automatically

from data (machine learning perspective)– Model fitting, Inference process

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Overview of Microarray Problem

Data Mining

Microarray Experiment

Image Analysis

Biology Application Domain

Experiment Design and Hypothesis

Data Analysis

Artificial Intelligence (AI)

Knowledge discovery in databases (KDD)

Data Warehouse

Validation

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Artificial Intelligence (AI) Community

• Issues:—Prior knowledge

(e.g., invariance)

—Model deviation from true model

—Sampling distributions

—Computational complexity

—Model complexity (overfitting)

Collect Data

Train Classifier

Choose Model

Choose Features

Evaluate Classifier

Design Cycle of Predictive Modeling

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Knowledge Discovery in Databases (KDD) Community

Database

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Data Mining and Image Analysis Steps

• Image Analysis— Normalization— Grid Alignment— Feature construction (selection and extraction)

• Data Mining— Statistics— Machine learning— Pattern recognition— Database techniques— Optimization techniques— Visualization— Prior knowledge

• Validation— Issues— Cross validation techniques

?

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IMAGE ANALYSIS

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Image Analysis: Normalization

Red Band

Green Band

Dynamic range of red band

Dynamic range of green band Solution: Reference points with

reference values

Beta Actin

PKG

HPRT

Beta 2 microglobulin

RubiscoAB binding protein

Major latex proteinhomologue (MSG)

Cattle and Soy Controls

Array of cattle and soy spiking controls. 50 ug of cattle brain total RNA was labeled with Cy3 (green).1 ul each of in vitro transcribed soy Rubisco (5 ng), AB binding protein (0.5 ng) and MSG (0.05 ng) were labeled with Cy5. The two labeled samples were cohybridized on superamine slides (Telechem, Inc.). To the right of each set of spots are five negative controls (water).

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Image Analysis: Grid Alignment

Solution: Manual, semi-automatic and fully automatic alignment based on fiducials and/or global grid fitting.

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Image Analysis: Feature Selection

Features: mean, median, standard deviation, ratios

Area: Sensitive to background noise

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Image Analysis: Feature Extraction

• Area is determined by image thresholding and used during feature extraction

Dist: 2004Box: 902Plane: 2632

1102

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DATA MINING

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Why Data Mining ? Sequence Example

• Biology: Language and Goals• A gene can be defined as a region of DNA.• A genome is one haploid set of chromosomes with the

genes they contain.• Perform competent comparison of gene sequences

across species and account for inherently noisy biological sequences due to random variability amplified by evolution

• Assumption: if a gene has high similarity to another gene then they perform the same function

• Analysis: Language and Goals• Feature is an extractable attribute or measurement

(e.g., gene expression, location)• Pattern recognition is trying to characterize data

pattern (e.g., similar gene expressions, equidistant gene locations).

• Data mining is about uncovering patterns, anomalies and statistically significant structures in data (e.g., find two similar gene expressions with confidence > x)

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Data Mining Techniques

S ta tis t ics M a ch in e lea rn ing

D a ta b ase te chn iqu es P a tte rn re co g n it ion

O p tim iza tio n te ch n iq u es

D a ta m in in g tech n iq u e s d ra w from

Visualization

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Statistics

Inductive Statistics

Statistics

Descriptive Statistics

Are two sample sets

identically distributed ?

Make forecast and inferences

Describe data

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Machine Learning

Supervised

Machine Learning

Unsupervised

Reinforced“Natural groupings”

Examples

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Pattern Recognition

Pattern Recognition

Linear Correlation and Regression

Neural Networks

Statistical Models

Decision Trees

Locally Weighted Learning

NN representation and gradient based optimization

NN representation and genetic algorithm based optimization

k-nearest neighbors, support vectors

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Database Techniques

• Database Design and Modeling (tables, procedures, functions, constraints)

• Database Interface to Data Mining System

• Efficient Import and Export of Data

• Database Data Visualization

• Database Clustering for Access Efficiency

• Database Performance Tuning (memory usage, query encoding)

• Database Parallel Processing (multiple servers and CPUs)

• Distributed Information Repositories (data warehouse)

MINING

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Optimization Techniques

• Highly nonlinear search space (global versus local maxima)

• Gradient based optimization

• Genetic algorithm based optimization

• Optimization with sampling

• Large search space

• Example: A genome with N genes can encode 2^N states (active or inactive states, regulated is not considered). Human genome ~ 2^30,000; Nematode genome ~ 2^20,000 patterns.

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Visualization

• Data: 3D cubes,distribution charts, curves, surfaces, link graphs, image frames and movies, parallel coordinates

• Results: pie charts, scatter plots, box plots, association rules, parallel coordinates, dendograms, temporal evolution

Pie chart Parallel coordinates

Temporal evolution

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Prior Knowledge from Experiment Design

Complexity Levels of Microarray Experiments:1. Compare single gene in a control situation versus a treatment

situation• Example: Is the level of expression (up-regulated or down-regulated)

significantly different in the two situations? (drug design application)• Methods: t-test, Bayesian approach

2. Find multiple genes that share common functionalities• Example: Find related genes that are dependent?• Methods: Clustering (hierarchical, k-means, self-organizing maps,

neural network, support vector machines)3. Infer the underlying gene and protein networks that are

responsible for the patterns and functional pathways observed• Example: What is the gene regulation at system level?• Directions: mining regulatory regions, modeling regulatory networks

on a global scaleGoal of Future Experiment Designs: Understand biology at the system

level, e.g., gene networks, protein networks, signaling networks, metabolic networks, immune system and neuronal networks.

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Types of Expected Data Mining and Analysis ResultsHypothetical Examples:• Binary answers using tests of hypotheses

—Drug treatment is successful with a confidence level x.

• Statistical behavior (probability distribution functions)—A class of genes with functionality X follows Poisson

distribution.• Expected events

—As the amount of treatment will increase the gene expression level will decrease.

• Relationships—Expression level of gene A is correlated with

expression level of gene B under varying treatment conditions (gene A and B are part of the same pathway).

• Decision trees —Classification of a new gene sequence by a “domain

expert”.

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VALIDATION

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Why Validation?

• Validation type:— Within the existing data— With newly collected data

• Errors and uncertainties:— Systematic or random errors— Unknown variables - number of classes— Noise level - statistical confidence due to noise— Model validity – error measure, model over-fit or under-fit — Number of data points - measurement replicas

• Other issues— Experimental support of general theories— Exhaustive sampling is not permissive

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Cross Validation: Example

• One-tier cross validation— Train on different data than test data

• Two-tier cross validation— The score from one-tier cross validation is

used by the bias optimizer to select the best learning algorithm parameters (# of control points) . The more you optimize the more you over-fit. The second tier is to measure the level of over-fit (unbiased measure of accuracy).

— Useful for comparing learning algorithms with control parameters that are optimized.

— Number of folds is not optimized.• Computational complexity:

— #folds of top tier X #folds of bottom tier X #control points X CPU of algorithm

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Summary

• Microarray problem— Computational biology — Major objective of microarray technology— Input and output of data analysis

• Data mining and image analysis steps— Image normalization, grid alignment, feature construction— Data mining techniques— Prior knowledge— Expected results of data mining

• Validation— Issues— Cross validation techniques