OSP FO# 11-081
AGENCY: Department of Health and Human Services (DHHS)/National Institutes of Health (NIH)/National Institute of Mental Health (NIMH)
OBJECTIVES: This program provides funding to develop advanced bioinformatics and statistical tools to integrate genomic, environmental and phenotypic data. This program seeks bioinformatics-based approaches to integrating multi-dimensional data sets with biologically meaningful outcomes. This may require the development of new methods for data reduction, development of new statistical tools, and/or development of new approaches to applying existing tools to the problems of integrating multi-dimensional data. Demonstrating the efficacy or proof of principle of proposed developments should be a component of any application. Desired outcomes would establish links between one or more of the underlying genomic factors, biological networks, and environmental factors influencing mental disorders.
This program creates an opportunity for investigators with expertise in integrating and mining multi-dimensional data to test research hypotheses relating biological and environmental etiologies of mental disorders. The data types of interest include expression profiling, genomics, proteomics, neuroimaging, cognitive, and behavioral markers of disease diagnosis, progression, and treatment response. Preference will be given to applications that incorporate three or more multi-dimensional data sets that have an origin in biological systems pertaining to mental health.
Examples of topics include, but are not limited to, the following:
- Developing bioinformatics tools to integrate data across platforms and modalities.
- Developing a bioinformatics based discovery engine for psychiatric disorders.
- Reducing data dimensions (e.g., dimensions of neuroimaging data or whole genome expression or sequencing data) using novel nonlinear filtering techniques.
- Analyzing integrated data using network-based analytical techniques such as machine learning and Bayesian graphic models.
- Analyzing integrated data using statistical models such as structural equation model.
- Conducting dimension selection with independent component analysis and then applying the selection to prediction.
- Conducting variable selection and model selection for multi-dimension and heterogeneous biological data.
- Using graph theory to construct biological networks.
- Integrating data via Bayesian methods and other principled statistical strategies.
- Applying nonlinear dimension reduction techniques such as subspace and manifold learning, kernel principal component analysis, multidimensional scaling, and index modeling, utilizing existing biological knowledge and specially designed for biological data.
For more information, please consult the program announcement below.
Letter of Intent (optional): May 9, 2011
Application Deadline: June 9, 2011
FUNDING INFORMATION: The NIMH intends to commit approximately $1.5 million in FY 2012 to fund up to 3 new grants. The number of awards is contingent upon NIH appropriations, and the submission of a sufficient number of meritorious applications. The total project period for a submitted application may not exceed three years.
Thomas Lehner, Ph.D.
Division of Neuroscience and Basic Behavioral Science
National Institute of Mental Health
6001 Executive Boulevard, Room 7190, MSC 9643
Bethesda, MD 20892-9643
Telephone: (301) 443-9869
REMARKS: NIH requires that applications to this program be submitted electronically through Grants.gov (http://www.grants.gov). After submission via Grants.gov, applications will be retrieved and processed by the NIH Commons system (https://commons.era.nih.gov/commons/index.jsp). In order to prepare a responsive application, PIs should download both the complete program guidelines (RFA-MH-12-020) and the corresponding application package from Grants.gov as well as the NIH Grants.gov Application Guide (http://grants.nih.gov/grants/funding/424/index.htm). PIs must also be registered Commons users.
For more information about Grants.gov and the NIH Commons, or to register as a Commons user, please contact either India Adams (firstname.lastname@example.org) or A. B. Effgen (email@example.com) in the Office of Sponsored Programs (OSP) at x3-4365. In addition, please contact the OSP Assistant Director assigned to your school or department as soon as possible to coordinate submission through the Grants.gov system.
Complete program guidelines and application material may be obtained from the web site listed above or from the Office of Sponsored Programs (OSP). Please distribute this notice to any faculty or staff members who might be interested in the information. For more information, please contact the OSP at X3-4365 or firstname.lastname@example.org, or visit the OSP web site at http://www.bu.edu/osp.