Genomic Pathway Visualizer _ Research _ Artificial Intelligence Laboratory _ Eller College of Management _ the University of Arizona

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Previous Research

Genomic Pathway Visualizer

Research Goal

To develop text mining and data mining techniques tosupport automated extraction and inference of regulatorypathways from biomedical li terature and experimental data.

Technological developments in genomic and proteomicresearch have led to an explosion of data available forbiomedical research. The sheer quantity of data generatedby high throughput technologies such as DNA microarrayhas exceeded the capacity of traditional data analysis

techniques to extract useful information. Meanwhile, rapidaccumulation of research publications makes it diffi cult tokeep abreast of new developments in the area.

The research goal of Arizona BioPathway is to develop novelmachine learning and Natural Language Processing (NLP)techniques to support effici ent and effective data and textanalysis in biomedical fields, particularly, the analysis ofgenetic regulatory pathways which are crucial for biologicalprocesses such as gene regulation and cancer development.Arizona BioPathway is also aimed at the creation of aframework for pathway-related knowledge integration andvisualization using a combination of various approaches.The ultimate goal of Arizona BioPathway is to providebiomedical researchers with a platform of pathway-relatedliterature abstraction, data analysis and knowledge integration, thus to support the developmentof scientific hypotheses and discovery of new knowledge.

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Funding

Funding for this research was received from the following sources:

1 R33 LM07299-0105/01/2002 -04/30/2005

National Institutes of Health/National Library ofMedicine

$1,320,000

GeneScene: A toolkit for gene pathway analysis

1R01 LM06919-01A1 2/15/2001 - 2/14/2004

National Institutes of Health/National Library ofMedicine

$500,000

UMLS Enhanced Dynamic Agents to Manage Medical Knowledge

IIS-9817473 5/1/99 - 4/31/2002

National Science Foundation $500,000

DLI Phase 2: High Performance Digital Library Classification Systems:From Information Retrieval to Knowledge Management


Eller College Home > MIS > Artificial Intelligence Laboratory > Research > Genomic Pathway Visualizer

mic Pathway Visualizer : Research : Artificial Intelligence Laboratory... http://ai.arizona.edu/research/bioinf

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Data Mining

P53 Microarray Data:Content: Gene expression measurement of p53 mutant cell lines (provided byAZCC)Gene expression measurements: 33Genes (Homo sapiens ORFs): 5,306Genes with greatest variations: 200

Yeast Microarray Data:Content: Microarray data of yeast cell cycle (Spellman et al. 1998)Gene expression measurements: 77Time series: 6Genes (S. cerevisiae ORFs): 6,177

Genes whose expression varied over the different cell-cycle stages: 800

Arabidopsis Micrarray data:Content: two high-quality microarray series of Arabidopsis athttp://www.weigelworld.orgGene expression measurements: 237 for development and 298 for abiotic stressGenes (Arabidopsis): 22,810

Arabidopsis Genome sequence relations:Content: gene relations extracted from genome sequence using four differentmethods in ProLink (http://dip.doe-mbi.ucla.edu/pronav)Relations:Phylogenetic profiling (PP): 132,637Rosetta Stone (RS): 989,795Gene neighbor (GN): 18,823Gene cluster (GC): 11,586

MDS Microarray data

Content: DNA methylation arrays from Arizona Cancer Center. It is derived from theepigenomic analysis of bone marrow specimens from healthy donors andindividuals with myelodysplastic syndrome (MDS).Measurements: 55 (10 normal and 45 tumor samples)Genes: 678

Ovarian Cancer Microarray dataContent: microarray-based measurements of DNA methylation from the GynecologicOncology tumor bank at the University of Iowa and made available through theArizona Cancer Center.Measurements: 114 (25 normal and 89 tumor samples)Genes: 6,560

Techniques:

A shallow parser based on closed class English words extracting noun phrase relationsA full parser using syntax-semantic hybrid grammar extracting verb relationsCo-occurrence analysis based on Concept Space, which generates asymmetric relations

between phrases ordered according to the strength of their relationConditional Random Field (CRF) methods for entity recognitionKernel-based learning methods for relation extraction and classificationFeature decomposition for entity and relation aggregationBayesian Network frameworks for integrating gene functional relations from multiple datasourcesOptimal search based feature subset selection methods for identifying marker genes forcancer classification


Team Members

Dr. Hsinchun Chen [email protected]

Dr. Zhu Zhang

Dr. Jesse Martinez Cathy Larson

Jiexun Li

Hua Su

Chun-Ju Tseng

Siddharth Kaza

Xin Li

Nichalin Suakkaphong

Yulei Zhang (Gavin)

Shailesh Joshi


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Publications

Text Mining Publications and Presentations

K. D. Quiones, H. Su, B. Marshall, S. Eggers, and H. Chen. User-centered evaluation of Arizona BioPathway: an information extraction,integration, and visualization system.IEEE Transactions on InformationTechnology in Biomedicine, 11(5): 527-536, 2007.

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B. Marshall, H. Su, D. McDonald, S. Eggers, and H. Chen. "AggregatingAutomatically Extracted Regulatory Pathway Relations." IEEETransactions on Information Technology in Biomedicine, 10:100-108,2006.

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B. Marshall, H. Su, D. McDonald, and H. Chen. Linking ontologicalresources using aggregatable substance identifiers to organize extractedrelations. In Proceedings of Pacific Symposium on Biocomputing, pp.162-173, 2005.

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G. Leroy, H. Chen. "GeneScene: An Ontology-Enhanced Integration ofLinguistic and Co-Occurrence Based Relations in Biomedical Texts,"Journal of The American Society for Information Science and

Technology (JASIST), 56: 457-468, 2005.

4.

D. McDonald, H. Chen, H. Su, and B. Marshall. "Extracting GenePathway Relations Using a Hybrid Grammar: The Arizona RelationParser," Bioinformatics 20:3370-3378, 2004.

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D.M. McDonald, H. Chen, G. Leroy, and H. Su. "Combining Ontologiesand Grammatical Relations to Yield Diverse Semantic Relations fromBiomedical Texts,Poster presentation at Pacific Symposium onBiocomputing, January 2004.

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G. Leroy, H. Chen, and J.D. Martinez. A Shallow Parser Based onClosed-class Words to Capture Relations in Biomedical Text.Journal ofBiomedical Informatics(JBI) 36:145-158, 2003.

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G. Leroy, H. Chen, J.Martinez, S. Eggers, R. Falsey, K. Kislin, Z. Huang,J. Li, J. Xu, D. McDonald, and G. Ng. "GeneScene: Biomedical Text andData Mining"Presented at the Third ACM and IEEE Joint Conference onDigital Libraries (JCDL-) May 27-31, 2003, Houston, Texas, 2003.

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G. Leroy and H. Chen. "Filling preposition-based templates to captureinformation for medical abstracts." In Proceedings of Pacific Symposiumon Biocomputing, pp. 350-361, 2002.

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Data Mining Publications and Presentations

J. Li, H. Su, H. Chen, and B. W. Futscher Optimal search-based genesubset selection from gene array data for cancer c lassification.IEEETransactions on Information Technology in Biomedicine, accepted,2006.

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Z. Huang, J. Li, H. Su, G. S. Watts, H. Chen "Large-scale regulatorynetwork analysis from microarray data: modified Bayesian Networklearning and association rule mining." Decision Support Systems:Special Issue on Decision Support in Medicine, forthcoming, 2006.

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J. Li, X. Li, H. Su, H. Chen, and D. W. Galbraith, "A framework ofintegrating gene relations from heterogeneous data sources: anexperiment onArabidopsis thaliana." Bioinformatics, 22:2037-2043,

2006.

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Z. Huang, H. Su, H. Chen Joint learning using multiple types of dataand knowledge, in H. Chen, S. Fuller, C. Friedman, and W. Hersh(Eds.), Medical Informatics: Knowledge Management and Data Mining inBiomedicine, Springer, p.593-624. 2005.

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Z. Huang, H. Chen, H. Su, B. Marshall, B. L. Smith, G. W. Watts, J. D.Martinez. Learning Genetic Pathways Using Bayesian Networks andQualitative Probabilistic Networks,Poster presentation at PacificSymposium on Biocomputing, January 2005.

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Z. Huang, H. Chen, H. Su, B. Marshall, B. L. Smith, G. W. Watts, J. D.Martinez.Learning Genetic Pathways Using Bayesian Networks and QualitativeProbabilistic Networks,Poster presentation at Pacific Symposium onBiocomputing, January 2004.

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Genomic Pathway Visualizer _ Research _ Artificial Intelligence Laboratory _ Eller College of Management _ the University of Arizona