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Transcript of Peter Bajcsy, PhD Automated Learning Group National Center for Supercomputing Applications...
Peter Bajcsy, PhDAutomated Learning GroupNational Center for Supercomputing ApplicationsUniversity of [email protected]
September 10, 2002
Data Mining in Bioinformatics
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Outline
• Introduction—Interdisciplinary Problem Statement—Microarray Problem Overview
• Microarray Data Processing—Image Analysis and Data Mining—Prior Knowledge—Data Mining Methods—Database and Optimization Techniques—Visualization
• 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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Bioinformatics, Computational Biology, Data Mining
• Bioinformatics is an interdisciplinary field about the the information processing problems in computational biology and a unified treatment of the data mining methods for solving these problems.
• Computational Biology is about modeling real data and simulating unknown data of biological entities, e.g.— Genomes (viruses, bacteria, fungi, plants, insects,…)— Proteins and Proteomes— Biological Sequences— Molecular Function and Structure
• Data Mining is searching for knowledge in data— Knowledge mining from databases— Knowledge extraction— Data/pattern analysis— Data dredging— Knowledge Discovery in Databases (KDD)
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Introduction: Problems 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
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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
Statistics
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Statistics Community
• Random Variables
• Statistical Measures
• Probability and Probability Distribution
• Confidence Interval Estimations
• Test of Hypotheses
• Goodness of Fit
• Regression and Correlation Analysis
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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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Microarray Data Mining and Image Analysis Steps• Image Analysis
— Normalization— Grid Alignment— Spot Quality Assurance Control— Feature construction (selection and extraction)
• Data Mining— Prior knowledge— Statistics— Machine learning— Pattern recognition— Database techniques— Optimization techniques— Visualization
• Validation— Issues— Cross validation techniques
?
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MICROARRAY IMAGE ANALYSIS
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Microarray Image Analysis
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DATA MINING OF MICROARRAY DATA
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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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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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PRIOR KNOWLEDGE
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Prior Knowledge: Experiment Design
• Microarray sources of systematic and random errors
• Feature selection and variability
• Expectations and Hypotheses
• Data cleaning and transformations
• Data mining method selection
• Interpretation
Collect Data
Choose Features
Data Cleaning and Transformations
Choose Model and Data Mining Method
Pri
or K
now
ledg
e
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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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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
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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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•Gene Expression Level in Control and Treatment situations
•Is the behavior of a single gene different in Control situation than in Treatment situation ?
Statistical t-test
• m – sample mean
• s – variance
Normalized distance Normalized distance t follows a Student distributionwith f degrees of freedom.
If t>thresh then the control and treatment data populations are considered to be different.
?
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MACHINE LEARNINGAND
PATTERN RECOGNITION
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Machine Learning
Supervised
Machine Learning
Unsupervised
Reinforcement“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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Unsupervised Learning and Clustering
• A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters.
• Examples of data objects:—gene expression levels, sets of co-regulated genes
(pathways), protein structures
• Categories of Clustering Methods—Partitioning Methods—Hierarchical Methods—Density-Based Methods
“Natural groupings”
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Unsupervised Clustering: Partitioning Methods
• K-means Algorithm partitions a set of n objects into k clusters so that the resulting intra-cluster similarity is high but the inter-cluster similarity is low.
• Input: number of desired cluster k
• Output: k labels assigned to n objects
• Steps:1.Select k initial cluster’s centers 2.Compute similarity as a distance between an object
and each cluster center3.Assign a label to an object based on the minimum
similarity4.Repeat for all objects5.Re-compute the cluster’s centers as a mean of all
objects assign to a given cluster6.Repeat from Step 2 until objects do not change their
labels.
Example: Centroid-Based Technique
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Unsupervised Clustering: Partitioning Methods
• K-medoids Algorithm partitions a set of n objects into k clusters so that it minimizes the sum of the dissimilarities of all the objects to their nearest medoid.
• Input: number of desired cluster k• Output: k labels assigned to n objects• Steps:1.Select k initial objects as the initial medoids2.Compute similarity as a distance between an
object and each cluster medoid3.Assign a label to an object based on the minimum
similarity4.Repeat for all objects5.Randomly select a non-medoid object and swap
with the current medoid it would decrease intra-cluster square error
6.Repeat from Step 2 until objects do not change their labels.
Example: Representative-Based Technique
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Unsupervised Clustering: Hierarchical Clustering
• Hierarchical Clustering partitions a set of n objects into a tree of clusters
• Types of Hierarchical Clustering
—Agglomerative hierarchical clustering– Bottom-up strategy of building clusters
—Divisive hierarchical clustering– Top-down strategy of building clusters
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Unsupervised Agglomerative Hierarchical Clustering• Agglomerative Hierarchical Clustering partitions a set
of n objects into a tree of clusters with a bottom-up strategy.
• Steps:1. Assign a unique label to each data object and form n
clusters2. Find nearest clusters and merge them3. Repeat Step 2 till the number of desired clusters is equal to
the number of merged clusters.
• Types of Agglomerative Hierarchical Clustering— The nearest neighbor algorithms (minimum or single-linkage algorithm, minimal
spanning tree)— The farthest neighbor algorithms (maximum or complete-linkage algorithm)
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Unsupervised Clustering: Density-Based Clustering
• Density-Based Spatial Clustering with Noise aggregates objects into clusters if the objects are density connected.
• Density connected objects:—Simplified explanation:P and Q are density connected
if there is an object O such that both P and Q are density connected to O.
—Aggregate P and Q if they are density connected with respect to R-radius neighborhood and Minimum Object criteria
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Supervised Learning or Classification
• Classification is a two-step process consisting of learning classification rules followed by assignment of classification label.
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Supervised Learning: Decision Tree
• Decision tree algorithm constructs a tree structure in a top-down recursive divide-and-conquer manner
Car Insurance: Risk Assessment
Age < 25 ?
Risk: LowRisk: High
Sports car ?Risk: High
Age Car Type
Risk
23 family High
17 sports High
43 sports High
68 family Low
32 truck Low
20 family High
yes no
noyes
Attributes
Answers
Visualization of Decision Boundaries
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Supervised Learning: Bayesian Classification
• Bayesian Classification is based on Bayes theorem and it can predict class membership probabilities.
• Bayes Theorem (X-data sample, H-hypothesis of data label)—P(H/X) posterior probability—P(H) prior probability
• Classification-maximum posteriori hypothesis
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Statistical Models: Linear Discriminant
• Linear Discriminant Functions form boundaries between data classes.
• Finding Linear Discriminant Functions is achieved by minimizing a criterion error function.
Linear discriminant function
Quadratic discriminant function
Finding w coefficients:
-Gradient Descent Procedures
-Newton’s algorithm
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Neural Networks
• Neural network is a set of connected input/output units where each connection has a weight associated with it.
• Phase I: learning – adjust weights such that the network predicts accurately class labels of the input samples
• Phase II: classification- assign labels by passing an unknown sample through the network
• Steps:1. Initial weights from [-1,1]2. Propagate the inputs forward3. Backpropagate the error4. Terminate learning (training) if (a) delta w < thresh or (b) percentage
of misclassified samples < thresh or (c) max number of iterations has been exceeded
Interpretation
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Support Vector Machines (SVM)
• SVM algorithm finds a separating hyperplane with the largest margin and uses it for classification of new samples
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DATABASE TECHNIQUESAND
OPTIMIZATION TECHNIQUES
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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
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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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Novel Visualization of Features
Feature Selection and Visualization
Feature Selection
Mean Feature Image
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Novel Visualization of Clustering Results
Isodata (K-means)Clustering
Class Labeling and Visualization
Mean Feature Image Label Image
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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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Error Detection: Example of Spot Screening
Mask Image – No ScreeningMask Image – Location and Size Screening
Mask Image – SNR Screening
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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
• Bioinformatics and Microarray problem— Interdisciplinary Challenges: Terminology— Understanding Biology and Computer Science
• Data mining and image analysis steps— Image Analysis— Experiment Design as Prior Knowledge — Expected Results of Data Mining— Which Data Mining Technique to Use?— Data Mining Challenges: Complexity, Data Size, Search Space
• Validation— Confidence in Obtained Results?— Error Screening— Cross validation techniques
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