This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.Molecular prediction and personalization is the future direction of medicine. With the rapid development of microarray technology, it has become increasingly promising to identify novel biomarkers for the diagnosis and prognosis of human disease. However, current molecular classifiers for the prediction of clinical outcomes are not optimized. The long list of molecular markers can be reduced and the prediction accuracy can be further increased by using appropriate data mining algorithms. There exists an urgent need for a general feature selection scheme for identifying potential diagnostic and prognostic markers from high-throughput data. Furthermore, the development of a suitable methodology for elucidating the complex molecular interrelations in disease progression is critical. The extracted biomarker patterns can be used to predict clinical outcome for individual patients. The goal of this proposal is to test the hypothesis that a systems biology framework combining bioinformatic, genomic, proteomic, and clinical approaches and information enables the construction of clinically important molecular prediction models. Specifically, we will (1) develop a general feature selection scheme to identify novel biomarkers from microarray data; and (2) optimize a network model to construct molecular prediction models for individualized clinical decision-making. The network model extracts molecular expression patterns to predict clinical outcome for individual patients. The long-term goals are twofold: 1) to develop a novel model framework for identifying important biomarkers that contain valuable information concerning the molecular mechanisms and therapeutic targets underlying disease, and 2) to make accurate predictions in individualized diagnosis, prognosis, and therapeutics. We anticipate that the proposed computational framework will fill gaps in current bioinformatics research. The proposed informatics framework and the identified molecular classifiers may have an important impact in bioinformatics and influence clinical care in general. We will disseminate the software system by providing web-based public access.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Exploratory Grants (P20)
Project #
5P20RR016440-08
Application #
7720595
Study Section
Special Emphasis Panel (ZRR1-RI-8 (01))
Project Start
2008-07-01
Project End
2009-06-30
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
8
Fiscal Year
2008
Total Cost
$243,230
Indirect Cost
Name
West Virginia University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
191510239
City
Morgantown
State
WV
Country
United States
Zip Code
26506
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