Training in Biomedical Discovery from Large Scale Data Sets

大规模数据集生物医学发现培训

基本信息

批准号：
7492915
负责人：
Timothy Palzkill
金额：
$ 12.4万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-30 至 2010-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7492915
关键词：
Advertising Advisory Committees Algorithms American Animal Model Annual Reports Apoptosis Applications Grants Appointment Archives Area Artificial Intelligence Arts Award Bacteria Behavior Biochemistry Bioinformatics Biological Biological Neural Networks Biological Process Biological Sciences Biology Biomedical Computing Biomedical Research Biophysics Biotechnology Case Study Cell physiology Cells Cellular biology Chromosome abnormality Collaborations Committee Members Communicable Diseases Communities Complex Computational Biology Computational Molecular Biology Computational Science Computational algorithm Computer Assisted Condition Consultations Counseling Country Cryoelectron Microscopy Cytoskeleton Data Data Set Data Sources Databases Depth Development Digital Signal Processing Discipline Disease Dissection Doctor of Philosophy Documentation Educational Curriculum Educational Status Educational process of instructing Educational workshop Endoplasmic Reticulum Engineering Enrollment Ensure Equilibrium Evaluation Event Facility Construction Funding Category Faculty Feedback Figs - dietary Fostering Foundations Fourier Transform Frequencies Funding Future Future Generations Gene Chips Gene Expression Generations Genes Genetic Genetic Medicine Genetic Models Genetics and Medicine Genome Genomics Goals Golgi Apparatus Grant Growth Hand Heredity Home environment Image Imagery Imaging technology In Situ Hybridization Individual Institution Interdisciplinary Study International Internships Intervention Interview Ion Transport Journals Knowledge Laboratories Language Learning Length Letters Libraries Life Machine Learning Malignant Neoplasms Maps Marketing Mathematics Measurement Medical center Membrane Mentors Methodology Methods Microscopy Mining Mitochondria Modeling Molecular Molecular Biology Molecular Structure Monitor Mus Names Nature Numbers Occupations Oncogenes Ontology Operative Surgical Procedures Optics Organism Paper Pathway interactions Pattern Pattern Recognition Peer Group Peer Review Persons Physiological Play Pliability Postdoctoral Fellow Preparation Principal Component Analysis Principal Investigator Printing Process Program Evaluation Protein Interaction Mapping Protein-Protein Interaction Map Proteins Proteomics Public Health Publications Published Comment Range Recruitment Activity Regulator Genes Reporting Research Research Personnel Research Project Grants Research Training Resolution Resources Rice Robotics Role Rotation Sampling Schools Science Scientist Secure Semantics Sequence Analysis Series Shapes Side Signal Pathway Signal Transduction Social Interaction Societies Source Specimen Standards of Weights and Measures Structure Students Suggestion System Systems Biology Techniques Technology Testing Texas Thinking Time Tissues Training Training Activity Training Programs Translating Universities Update Vision Week Work Writing Yeasts base biomedical scientist cancer cell cancer type career computer code computer program computer science computerized data processing computerized tools concept cost data acquisition data integration data management data mining data modeling day density design desire drinking experience falls functional genomics gulf coast human disease image processing image reconstruction indexing interdisciplinary collaboration interest knowledge base lectures macromolecular assembly macromolecule mecarzole member models and simulation novel novel strategies nucleocytoplasmic transport parallel architecture peer posters pre-doctoral programs relating to nervous system repository shared memory simulation skills software development statistics structural biology symposium theories tool vector

项目摘要

DESCRIPTION (provided by applicant): The development of genomics, proteomics and advanced imaging 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 is data-driven, and in which data are the source of hypotheses as well as the means for testing them. These masses of data 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, proteomics and 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 systems from the molecular to the organism 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.

描述（申请人提供）：基因组学、蛋白质组学和先进成像技术的发展导致了大量生物数据的积累。随着大规模数据集在生物医学研究中占据主导地位，我们正在接近一种范式转变，其中发现过程是数据驱动的，数据是假设的来源以及测试假设的手段。这些海量数据是丰富的信息来源；然而，提取有意义的信息可能是一项艰巨的挑战，并且常常成为发现过程的瓶颈。因此，迫切需要对了解数据、数据生成方式以及数据用途的科学家进行跨学科培训。此外，这些科学家必须成为分析大型数据集所需的新计算工具的开发人员和高技能用户。该计划的目标是培养学生精通以下领域： 1. 数据采集。这将包括基因组学、蛋白质组学和成像方法的知识。 2.计算。这将包括数学和统计算法的知识、有效计算机代码的实现以及对关系数据库、演绎数据库和其他数据库中数据仓库方法的强调。 3.数据整合。这是一个关键领域，涉及从不同空间和时间尺度的异构数据集中提取有用信息。它将包括从分子到有机体水平的系统建模和模拟方法的知识。还将强调计算数据挖掘方法。该计划的核心将是在至少两名来自不同学科（即计算/数学和生物医学科学）的导师的指导下，对跨学科团队进行基于研究的培训。培训活动将包括专门的教学课程以及研讨会、期刊俱乐部和师生静修会。这将是一个跨机构培训计划，教职员工来自计算机科学、统计学、遗传学和医学等各个部门，分布在休斯顿地区墨西哥湾沿岸联盟的五个参与机构中。对能够管理和从大数据集中提取信息的科学家进行培训将极大地促进传染病和癌症等领域的生物发现，因此该培训计划将对公众健康产生直接、积极的影响。