1. Overview of Biomedical Informatics
Vipin Kumar
University of Minnesota
Team Members: Michael Steinbach, Rohit Gupta, Gowtham Atluri, Gang Fang, Gaurav Pandey, Sanjoy Dey, Vanja Paunic
Collaborators: Brian Van Ness, Bill Oetting, Gary L. Nelsestuen, Christine Wendt, Piet C. de Groen, Michael Wilson
Research Supported by NSF, IBM, BICB-UMR, Pfizer
Nov 12th, Understanding Biotechnology – The Science of the ‘Omics’ 1

2. Biomedical Informatics
Recent technological advances are helping to generate large amounts of biomedical data
Data from high-throughput experimental techniques
Gene expression data
Biological networks
Proteomics and metabolomics data
Single Nucleotides Polymorphism (SNP) data
Electronic Medical Records
IBM-Mayo clinic partnership has created a DB of 5 million patients
Great potential benefits from the analysis of these large-scale data sets:
Automated analysis of patients history for customized treatment
Discovery of biomarkers for complex diseases and other phenotypes
Cheminformatics and drug discovery 2 2

3. Large-scale Data is Everywhere!
There has been enormous data growth in both commercial and scientific databases due to advances in data generation and collection technologies
New mantra
Gather whatever data you can whenever and wherever possible.
Expectations
Gathered data will have value either for the purpose collected or for a purpose not envisioned.
Homeland Security
Business Data
Geo-spatial data
Computational Simulations
Sensor Networks
Scientific Data

4. Data Mining Data Automated techniques for analyzing large data sets.
Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems.
Predictive Modeling
Clustering
Association Rules
Anomaly Detection
Milk
Data 4

5. Model for predicting credit worthiness
Predictive Modeling: Classification
Find a model for class attribute as a function of the values of other attributes
Model for predicting credit worthiness
Class

6. Discovering biomarkers
Gene Expression Data
Given: n labeled subjects, each with expression levels of p genes
Objectives: build a predictive model to identify cancer subtypes
Genes
Classical study of cancer subtypes
Golub et al. (1999)
identification of diagnostic genes
SNP Data
Given: n labeled subjects, each with genotypes of p SNPs
Objectives: build a model using genotypes to predict labels.
SNP 1
SNP 2
SNP 3
……..
…….
Class
Patient 1 AC GT AA 1
Patient 2 GG
……… ..
Patient n CC AG

7. Predicting short-term vs. long-term survivors among myeloma subjects
3404 SNPs (Selected according to potential relevance to Myeloma)
Cases: 70 Patients who survived shorter than 1 year
Controls: 73 Patients survived longer than 3 years
SNPs
cases
Brian Van Ness et al,
Genomic Variation in Myeloma: Design, content and initial application of the Bank On A Cure SNP Panel to detect associations with progression free survival,
BMC Medicine, Volume 6, pp 26, 2008.
controls

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
Applications:
Finding groups of similar genes or proteins based upon their expression profiles
Clustering of patients based on phenotypic and genotypic factors for efficient disease diagnosis
Market Segmentation
Document Clustering
Courtesy: Michael Eisen
Michael Eisen et al, 1999 8

9. Association Pattern Discovery
Given a set of records each of which contain some number of items from a given collection;
Produce dependency rules which will predict occurrence of an item based on occurrences of other items.
Biological applications
Identifying functional modules in protein interaction networks
Identifying transcription modules in gene expression data
Identifying biological entities associated with disease phenotypes
Biomarker discovery from genomic data, e.g. gene expression, Single-nucleotide polymorphism(SNP), metabolite data etc.
Rules Discovered:
{Milk} --> {Coke}
{Diaper, Milk} --> {Beer}

10. Discovery of Discriminative Patterns from Lung Cancer Gene Expression Data
67 Normal samples, 102 cancer patients, 8787 genes
[Stearman et al. 2005], [Su et al. 2007], [Bhattacharjee et al. 2001]
Visualization of a size-10 pattern using a new discriminative pattern finding technique
Gang Fang, Rui Kuang, Gaurav Pandey, Michael Steinbach, Chad L. Myers and Vipin Kumar, Subspace Differential Coexpression  Analysis: Problem Definition and A General Approach, In the Proceedings of the 15th Pacific Symposium on Biocomputing (PSB), pp , 2010.
Enriched with the TNF/NFkB signaling pathway
which is well-known to be related to lung cancer
P-value: 1.4*10-5 (6/10 overlap with the pathway)

11. Discriminative Metabolite Patterns from Liver Cirrhosis Data
41 alcoholic liver cirrhosis (row 1-41), 19 controls (row 42-60), 3610 metabolites
Data from Gary Nelsestuen et al.
A sample group of five metabolites having very similar (in relative terms) intensity values in cases, but mostly absent in controls.
(a) The rank values (black is 10, white is 0),
(b) original intensity values.
Gaurav Pandey, Gowtham Atluri, Michael Steinbach, Chad L. Myers and Vipin Kumar, An Association Analysis Approach to Biclustering, Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), , 2009.
(a)
(b)

12. Summary
Data mining techniques hold great promise for data-driven hypothesis generation in the biomedical domain.
Ample scope exists for the development and application of novel techniques for the analysis of different types of biomedical data.

13. For further information…
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Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining, Addison-Wesley, 2005.