SBIR/STTR Research Award

Integration and analysis tools for protein interaction networks

Award identifier 3edbc6224d2bd7b80f0eca5ba6d9b36ff78fb02af0a99a8901c065e4a0130eafAwarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$199,950

Program and phase

SBIR · Phase I

Award year

2006

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
Acelot, Inc.
Proposal award date
Not reported
Contract end date
Not reported
UEI
CC15RKJHFRE1
Solicitation number
PHS2006-2
Topic code
Not reported

Research abstract

DESCRIPTION (provided by applicant): The growing size and diversity of biological databases has necessitated the design of new scalable tools that can search across multiple databases and integrate information from multiple data sources. We propose to develop software for integrating and understanding protein-protein interactions, a fundamental problem in biology. A set of tools will be developed for constructing large-scale probabilistic networks of protein interactions using data sources such as microarrays, bioimages, GO annotations, genomic data, literature, and experimental data. The techniques will be based on Bayesian networks (BN) and Support Vector Machines (SVM), and will be made scalable to large datasets. The second goal is to develop tools for analyzing interaction networks for pathway discovery, motif finding, and function identification. These tools will be based on current research in the areas of graph algorithms, bioinformatics, machine learning, and databases. We will target two model organisms: S. cerevisiae (yeast) and C. elegans (worm). The quality of the constructed networks will be evaluated with known protein interactions for these species. Scalability tests will be performed with the worm interactome that is about ten times larger than the yeast interactome. The developed tools will be compatible with current standards and integrated into a database backend. The resulting software will enable assimilation of heterogeneous biological data with the ultimate goal of increased understanding of fundamental processes in molecular biology. The goal of this Phase I project is to prove the feasibility of constructing and analyzing probabilistic protein interaction networks in a scalable manner using new algorithms. The integration of diverse data sources such as microarrays, genomics, literature, and high-throughput experiments into pathways will facilitate the study the biological processes behind human diseases. The understanding of protein interactions within a pathway and interactions between pathways will lead to the selection of appropriate targets for therapeutic intervention, and eventually to cheaper and faster drug discovery.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

7 SBIR/STTR records share this UEI (2006–2016). Reported award amounts total $2,252,805. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Health and Human Services, National Science Foundation

Agency intelligence

Department of Health and Human Services

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.