Proteomics software package for the detection of cancer
Proteomics software package for the detection of cancer
批准号:
6791160
负责人:
Maciek Sasinowski
金额:
$95.95万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-04-01 至 2006-03-31
关键词:
bioinformaticsclinical researchcomputational biologycomputer program /softwarecomputer system design /evaluationdata collectiondata managementelectrospray ionization mass spectrometrygene expression profilinghuman datahuman subjectinformaticsinformation retrievalmass spectrometrymethod developmentneoplasm /cancer diagnosisphlebotomyproteomics
中文摘要
描述(由申请人提供):
英文摘要
DESCRIPTION (provided by applicant):
In the Phase II of this SBIR project we will develop a modular and extensible exploratory software tool for signal conditioning and data mining of profiling proteomics MS necessary for the development of cancer diagnostics. The software will allow researchers to construct and validate iterative classification methods for mass spectrometry (MS) data. The methods will be developed for surface enhanced laser desorption ionization (SELDI) MS, but the algorithms themselves and the modular framework will be applicable to analysis and classification of other types of MS data, such as matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI). This classification software will be capable both of diagnosing disease and identifying clinically significant features in MS records. The user interface will be based on the existing INCOGEN VIBE software, which was developed to provide a user-friendly, extensible framework for data analysis. The proposed software will be comprised of a framework and modules that enable data management and visualization, signal processing and feature selection, and classification and cross-validation. Each module will represent a computational tool that has been evaluated in the Phase I project or during the research phase of the Phase II project and has been selected for inclusion based on criteria such as numerical robustness, classification error and processing time. The modular architecture of the developed tool will ensure extensibility of the software package and provide tools for construction of flexible classifiers that are adaptable to data acquisition protocols, experimental equipment and protein profiling bio-chemistry. Our Phase I research suggests that such architecture is optimal for research and development of profiling MS essays both in clinical and academic environment because of the required level of flexibility and interactivity. The software package will be licensed to researchers as well as to MS hardware manufacturers for inclusion with their instruments.
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海外基金