Training in Biomedical Discovery from Large Scale Data Sets
Training in Biomedical Discovery from Large Scale Data Sets
批准号:
7293590
负责人:
Timothy Palzkill
金额:
$11.86万
依托单位国家:
美国
项目类别:
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-30 至 2010-07-31
关键词:
AlgorithmsAreaBiologicalBiomedical ResearchCommunicable DiseasesDataData SetData SourcesDatabasesDevelopmentDisciplineFacultyGenetic MedicineGenetics and MedicineGenomicsGoalsImageInstitutionJournalsKnowledgeMalignant NeoplasmsMentorsMethodsMolecularProcessProteomicsPublic HealthRangeResearchScienceScientistSourceStudentsTechnologyTestingTrainingTraining ActivityTraining Programsbasecomputer codecomputer sciencecomputerized toolsdata acquisitiondata integrationdata mininggulf coastmodels and simulationprogramsstatistics
中文摘要
点击翻译按钮获取中文摘要
英文摘要
The development of genomics, proteomics and advancedimaging technology has resulted in the
accumulation of vast amounts of biological data. As large scale data sets become predominant in
biomedical research, we are approaching a paradigm shift in which the process of discovery isdata-driven,
and in which data are the source of hypotheses as well as the means for testing them. These masses ofdata
are rich sources of information; however, extracting meaningful information can be a daunting challenge, and
often presents a bottleneck for the discovery process. Thus, there is a pressing need for interdisciplinary
training of scientists who understand the data, how they are generated, and what they are used for. In
addition, these scientists must become developers and highly skilled users of the new computational tools
necessary to analyze large data sets. The goal of this program is to train students to become proficient in the
following areas: 1. Data acquisition. This will include knowledge of the methods of genomics, proteomicsand
imaging. 2. Computation. This will include knowledge of mathematical and statistical algorithms,
implementation of effective computer codes as well as an emphasis on methods of data warehousing in
relational, deductive and other databases. 3. Data integration. This is a critical area that involves extracting
useful information from the heterogeneous data sets at various spatial and temporal scales. It will include
knowledge of methods of modeling and simulation of systemsfrom the molecular to the organismal level.
There will also be an emphasis on computational data mining methods. The core of the program will be
research-based training in interdisciplinary teams under the guidance of at least two mentors from disparate
disciplines (i.e., computational/mathematical and biomedical sciences). Training activities will consist of
specialized didactic coursework as well as seminars, journal clubs and a student-faculty retreat. This will be
a cross-institutional training program with faculty drawn from departments ranging from computer science
and statistics to genetics and medicine, in five participating institutions in the Gulf Coast Consortia in the
Houston Area.The training of scientists equipped to manage and extract information from large data sets will
greatly facilitate biological discovery in areas such as infectious disease and cancer and therefore this
training program will have a direct, positive impact on public health.
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Discovery of Carbapenemase Inhibitors Using DNA-Encoded Chemical Libraries
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Analysis of metallo-beta-lactamase sequence constraints at high resolution
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批准号:8829744
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资助金额:$38.56万
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财政年份:2013
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Analysis of metallo-beta-lactamase sequence constraints at high resolution
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批准号:8660631
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资助金额:$38.0万
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财政年份:2013
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依托单位:
Analysis of metallo-beta-lactamase sequence constraints at high resolution
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批准号:8557707
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项目类别:
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批准号:8112233
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资助金额:$19.56万
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财政年份:2011
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依托单位:
Development of Protein-Based Beta-lactam Antibiotic Resistance Diagnostics
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财政年份:2010
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依托单位:
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批准号:7224610
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项目类别:
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资助金额:$23.6万
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批准号:7668498
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资助金额:$5.97万
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依托单位:
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