1. Data Mining for Finding Connections of Disease and Medical and Genomic Characteristics
Vipin Kumar
William Norris Professor and Head, Department of Computer Science
Michael Steinbach
Department of Computer Science

2. Increasing Amounts of Medical and Genomic Data
Electronic medical records are becoming increasingly common
Automated analysis of patient information is now possible
Obtaining genomic information is increasingly affordable
SNPs offer the potential of tests for disease or susceptibility for disease

3. Statistical Association Analysis
Various Techniques Currently Used to Analyze the Relationship Between SNPs and Disease
Statistical Association Analysis
Chi-Square and Odds ratio
Logistic Regression
Multifactor Dimensionality Reduction
Attribute selection and construction followed by classification

4. Challenges Facing Current Techniques
Statistical Association Analysis
Often lacks power, even for single SNP association
Combinatorial explosion when used for screening pairs, triples, etc.
Logistic Regression
Does not work well when many attributes
Multifactor Dimensionality Reduction
Impractical for many attributes due to combinatorial nature
General Challenges
Disease or susceptibility to disease often results from interactions among many genetic, phenotypic, and environmental factors
Noise and nonlinear interactions
The Challenges of Whole-Genome Approaches to Common Diseases, Moore and Ritchie, JAMA, 2004

5. General Approach Using Data Mining Techniques
Create a data set that records the presence and absence of
Phenotypic characteristics
Genetic characteristics (SNPs)
Disease
Apply association analysis to find groups of phenotypic and genetic characteristics that are highly associated with disease
Uses characteristics of the patterns to prune the search space
Clustering and classification can also be applied

6. Traditional Association Analysis
Association analysis: Analyzes relationships among items (attributes) in a binary transaction data
Example data: market basket data
Data can be represented as a binary matrix
Applications in business and science
Two types of patterns
Itemsets: Collection of items
Example: {Milk, Diaper}
Association Rules: X  Y, where X and Y are itemsets.
Example: Milk  Diaper
Set-Based Representation of Data
Binary Matrix Representation of Data

7. The Need for Error-Tolerant Itemsets
An error-tolerant itemset (ETI) can have a fraction  of the items missing in each transaction.
Example: see the data in the table
Let  = 1/4. In other words, each transaction needs to have 3/4 (75%) of the items.
X = {i1, i2, i3, i4} and Y = {i5, i6, i7, i8} are both ETIs with a support of 4.
Algorithms to find ETIs are still in development
You can think of these ETIs as blocks in the data matrix

8. ETIs in For Finding Patterns in Phenotypic and Genomic Data
ETIs consist of
A set of patients and
A set of attributes
such that
The block is relatively dense
These blocks identify sets of patients that are highly associated with certain sets of attributes and vice-versa
If most of these patients share a disease, then these attributes (genetic and/or phenotypic) are candidate markers for the disease
X: Set of patients
Y: Set of attributes, i.e., SNPs, medical characteristics

9. Example: Using ETIs to Find Rules
Can use ETIs to find better association rules
Example: Mushroom data set
Classifies 8192 mushrooms as poisonous (3916) or not (4208)
117 other attributes, such as color, odor, etc.
Comparison of rules based on frequent itemsets or ETIs
One rule is {29, 48, 90} → p
Individual correlations are 0.62, 0.54, 0.18
With traditional association analysis, this rule has a confidence of 1 and a support of 576.
This corresponds to a correlation of 0.18
If we require only two of the three items, we still have a confidence of 1, but the support is 3312.
This corresponds to a correlation of 0.86
Maximum single item correlation is 0.78

10. Generalizing ETIs to Blocks in a Data Matrix
Dense blocks consist of
A set of objects and
A set of attributes
such that
The block is relatively dense
These blocks identify sets of objects that are highly associated with certain sets of attributes and vice-versa
For gene expression data, this identifies transcription modules
Group of genes that are coexpressed together under a set of conditions
X: Genes
Y: Conditions under which genes are expressed
Entries of matrix are the level of a gene’s expression under a condition

11. Techniques for Finding Relatively Dense Blocks in a Data Matrix
Algorithms for finding Error-Tolerant Itemsets
More work needed to develop algorithms
We are currently using support envelopes
Support Envelopes: A Technique for Exploring the Structure of Association Patterns, Steinbach, Tan, Kumar, KDD, 2004
Subspace clustering
Similar in spirit to association analysis
Subspace clustering for high dimensional data: A review, Parsons , Haque , Liu SIGKDD Explorations, 2004
Co-clustering
Information-Theoretic Co-Clustering, Dhillon, Mallela, Modha, KDD 2003
We are currently exploring approaches to extend co-clustering for ETIs
Variety of other approaches
Matrix factorization
Graph-partitioning

12. Support Envelopes
A support envelope contains all association patterns involving m or more transactions and n or more items
By association patterns we mean
Itemsets and variants (frequent, maximal, closed)
Error Tolerant Itemsets (ETIs)
An example of a support envelope involving characteristics of mushrooms. One of the attributes is, ‘gill-color:buff’, which occurs in records, every one of which occurs with 13 other items (one of which is the attribute,‘poisonous’)

13. Visualizing Support Envelopes for Mushroom
One of the support envelopes (576, 23) is denser than its surrounding neighbors.

14. Using Weak Associations to Find Patterns
Apply the following principle: If most pairs of attributes or objects in a set have a pairwise connections, then there is likely to be a strong association among them even if the pairwise associations are weak.
The hyperclique pattern uses this principle
Hyperclique Pattern Discovery, Xiong Tan, and Vipin Kumar, to appear DMKD, 2006.
Good for removing noise
Enhancing Data Analysis with Noise Removal, Hui Xiong, Gaurav Pandey, Michael Steinbach, Vipin Kumar, TKDE, 2006
We have recently developed more general pairwise patterns
Custom Itemset Patterns, Steinbach and Kumar, submitted to KDD 2006

15. Application of Classification Techniques
Techniques must work with noisy, sparse, high- dimensional data
Success of multifactor dimensionality reduction indicates the usefulness of attribute selection and attribute creation
ETI and related patterns offer an alternative for feature extraction
Classification based on association analysis
SVM with the proper kernel