SBIR-STTR Award

Neural Systems That Extract Information from Databases
Award last edited on: 6/17/02

Sponsored Program
SBIR
Awarding Agency
NIH : NCI
Total Award Amount
$80,768
Award Phase
1
Solicitation Topic Code
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Principal Investigator
J C Bhandari

Company Information

Novel Pharmaceuticals

Box 14588 85 TW Alexander Drive
Research Triangle Pk, NC 27709
   (919) 582-8800
   N/A
   N/A
Location: Single
Congr. District: 04
County: Durham

Phase I

Contract Number: 1R43CA062795-01
Start Date: 00/00/00    Completed: 00/00/00
Phase I year
1994
Phase I Amount
$80,768
Our company will develop a method of automatically programming neural networks to extract needed information from dataYbases of biological and chemical information. We will pursue the creation of a framework for training a neural network to predict the toxic and carcinogenic properties of chemicals using data stored in the Organ Toxicity Database. The neural network will be trained to recognize patterns and correlations between chemical structure/ substructures, physicochemical properties, and toxicologic/carcinogenic findings stored in the electronic compilation of a large subset of NTP TR and NTP TOX documents. A system which automatically selects the pertinent data fields for accurately predicting toxicity and then trains a neural network will be developed.The ability of the resulting neural network to predict the carcinogenic and toxic properties of known liver and kidney toxicants and carcinogens will be evaluated during Phase I. If accurate prediction is achieved, we will build a generalized framework for extending our techniques to other types of biological and chemical databases during Phase II. The value of many databases will be greatly increased by the availability of methods to automatically create neural networks which extract needed information for various applications. For example, our toxicity prediction system would reduce the exposure of the general public to possibly hazardous chemicals, would allow chemical manufacturers to quickly determine the hazards of new and exiseng products, and would save enormous amounts of time and money currently spent on animal testing. Each application developed using our framework could offer similar benefits.Commercial ApplicationsThe general purpose learning capability of our cascadedcompetitive/feed forwardneural network, combined with the ability to automatically evaluate and discard useless data, allows our system to extract hidden information from arbitrary chemical and biological databases. In particular, our pilot project to create a neural system to predict the toxicity of new chemicals can help companies save time and money in testing without subjecting the general public to increased risks of exposure to carcmogens.National Cancer Institute (NCI)

Phase II

Contract Number: ----------
Start Date: 00/00/00    Completed: 00/00/00
Phase II year
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Phase II Amount
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