1. Techniques for Finding Patterns in Large Amounts of Data: Applications in Biology
Vipin Kumar
William Norris Professor and Head, Department of Computer Science

2. Why Data Mining? Commercial Viewpoint
Lots of data is being collected and warehoused
Web data
Yahoo! collects 10GB/hour
purchases at department/ grocery stores
Walmart records  20 million transactions per day
Bank/Credit Card transactions
Computers have become cheaper and more powerful
Competitive Pressure is Strong
Provide better, customized services for an edge (e.g. in Customer Relationship Management)

3. Why Data Mining? Scientific Viewpoint
Data collected and stored at enormous speeds (GB/hour)
remote sensors on a satellite
NASA EOSDIS archives over 1-petabytes of Earth Science data per year
telescopes scanning the skies
Sky survey data
gene expression data
scientific simulations
terabytes of data generated in a few hours
Traditional techniques infeasible for raw data
Data mining may help scientists
in automated analysis of massive data sets
in hypothesis formation

4. Origins of Data Mining
Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems
Traditional Techniques may be unsuitable due to
Enormity of data
High dimensionality of data
Heterogeneous, distributed nature of data
Statistics/ AI
Machine Learning/
Pattern Recognition
Data Mining
Database systems

5. Data Mining Tasks... Data Milk Clustering Predictive Modeling
Anomaly Detection
Association Rules
Milk

6. Data Mining for Biology
Explosion of various types of biological data in recent years:
Protein sequences (SwissProt, MIPS)
Genome sequences (TIGR)
Gene expression (Stanford MicroArray Database)
Metabolic pathways (KEGG, HumanCyc)
Automated techniques for knowledge discovery are crucial for deriving useful information from these data sets
Identification of all genes on a genome
Prediction of protein function and structure from its amino acid sequence
Inference of pathways and regulatory networks
Drug discovery and identification of putative binding sites in protein structures

7. How can data mining help biologists?
Data mining particularly effective if the pattern/format of the final knowledge is presumed; common in biology:
Protein complexes (clustering and association patterns)
Gene regulatory networks (predictive models)
Protein structure/function (predictive models)
Motifs (association patterns)
We will look at two examples:
Clustering of ESTs
Identifying protein functional modules from protein complexes

8. Clustering
Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups
Inter-cluster distances are maximized
Intra-cluster distances are minimized

9. Applications of Cluster Analysis
Understanding
Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations
Summarization
Reduce the size of large data sets
Clustering precipitation in Australia

10. Clustering of ESTs in Protein Coding Database
Laboratory Experiments
New Protein
Functionality of
the protein
Similarity Match
Researchers
John Carlis
John Riedl
Ernest Retzel
Elizabeth Shoop
Clusters of Short Segments
of Protein-Coding
Sequences (EST)
Known Proteins

11. Expressed Sequence Tags (EST)
Generate short segments of protein-coding sequences (EST).
Match ESTs against known proteins using similarity matching algorithms.
Find Clusters of ESTs that have same functionality.
Match new protein against the EST clusters.
Experimentally verify only the functionality of the proteins represented by the matching EST clusters

12. EST Clusters by Hypergraph-Based Scheme
662 different items corresponding to ESTs.
11,986 variables corresponding to known proteins
Found 39 clusters
12 clean clusters each corresponds to single protein family (113 ESTs)
6 clusters with two protein families
7 clusters with three protein families
3 clusters with four protein families
6 clusters with five protein families
Runtime was less than 5 minutes.

13. 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

17. Where are the parts located?
How many roles can these play?
How flexible and adaptable are they mechanically?
What are the shared parts (bolt, nut, washer, spring, bearing), unique parts (cogs, levers)? What are the common parts -- types of parts (nuts & washers)?
Where are the parts located?
Which parts interact?
© Mark Gerstein, Yale

26. Data Mining Book For further details and sample chapters see