MIT EECS/Whitehead/Broad Training Program in Computational Biology(RMI)

麻省理工学院 EECS/Whitehead/计算生物学广泛培训计划 (RMI)

• 批准号: 7483074
• 负责人: David K Gifford
• 金额: $ 13.63万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7483074
• 关键词: Algorithms; Appendix; Applied Research; Architecture; Area; Artificial Intelligence; Bioinformatics; Biological; Biological Neural Networks; Biological Phenomena; Biology; Biomedical Research; Biotechnology; Brain; Budgets; Cataloging; Catalogs; Cellular biology; Class; Clinical Data; Cognitive Science; Collaborations; Communication; Competence; Complex; Computational Biology; Computational Molecular Biology; Computer Analysis; Computer Graphics; Computer Security; Computer Simulation; Computer Systems; Computers; Data; Data Analyses; Data Sources; Depth; Development; Digital Computers; Discipline; Disease; Doctor of Philosophy; Drops; Educational Status; Educational process of instructing; Electrical Engineering; Electronics; Elements; Engineering; Ensure; Environment; Exposure to; Facility Construction Funding Category; Faculty; Fostering; Foundations; Genes; Genetic; Genomics; Goals; Graduate Education; Grant; Head; Health; Housing; Human; Human Biology; Human Genome; Human Resources; Industry; Institutes; Institution; Intelligence; Interdisciplinary Study; Interview; Joints; Journals; Knowledge; Laboratories; Laboratory Research; Language; Lead; Learning; Machine Learning; Maps; Master of Science; Master' s Degree; Mathematics; Medical; Mentors; Mentorship; Methods; Mind; Minor; Molecular Biology; Monitor; Nature; Numbers; Oral; Oral Examination; Paper; Participant; Persons; Pharmacologic Substance; Positioning Attribute; Postdoctoral Fellow; Principal Investigator; Professional counselor; Programming Languages; Proteins; Publications; Qualifying; Randomized; Range; Reader; Recommendation; Regulation; Regulator Genes; Reporting; Representations, Knowledge (Computer); Research; Research Personnel; Research Project Grants; Research Training; Running; Scheme; Science; Scientist; Sequence Analysis; Site; Societies; Solid; Staging; Structure; Structure/Function Nuclei; Students; Supervision; System; Systems Biology; Systems Theory; Techniques; Technology; Thinking; Time; Training; Training Programs; Transcriptional Regulation; Universities; Vision; Volition; Week; Work; Writing; Yeasts; base; cancer diagnosis; combinatorial; computer network; computer science; cost; cryptography; day; design; experience; functional genomics; gene discovery; interdisciplinary approach; interest; knowledge base; member; novel strategies; parallel processing; pre-doctoral; predictive modeling; professor; programs; protein structure; quality assurance; research study; residence; response; scientific computing; skills; speech recognition; success; symposium; systems research; theories; tool

DESCRIPTION (provided by applicant): We propose to establish a new interdisciplinary research training program in Computational Biology as a collaborative effort between MIT, the Whitehead Institute, and the Broad Institute. The goal of this program is to train computer science students to be effective interdisciplinary scientists, working as team members with biologists to develop new algorithms, tools, and approaches for analyzing experimental biological data and expressing this analysis in the form of principled predictive models. The program faculty will consist of five MIT EECS faculty in Computer Science, four MIT/Whitehead faculty members in Biology, and the head the MIT Broad Institute. The major research disciplines of this program include: the development of new approaches and algorithms for the analysis of data from biological experiments, approaches for the principled design of biological experiments based upon past data, the construction of computational models that explain complex biological phenomenon, and the development of approaches for interpreting clinical data relevant to human health and disease. It is proposed that four pre-doctoral trainees be supported in this program. We have been running an informal training program in this area for over three years, and our students to date have made substantial contributions to the field. Among our recent graduates are faculty at Stanford, Princeton, Duke, and CMU. Our pool of applicants is unusually strong, with 574 applicants in 2004 in the relevant sub-area of computer science. Trainees in our proposed research training program will have a very rigorous technical and quantitative foundation from the MIT graduate program in Computer Science, combined formal interdisciplinary course work and a co mentorship arrangement between a Computer Science and a Biology faculty member. The strong technical skills present in our pre-doctoral students have provided an excellent foundation for the creation of ground breaking new approaches and algorithms in computational biology. In addition, we have run off-site interdisciplinary summer retreats for the past three years that have been very effective at catalyzing productive pre-doctoral research.

描述（由申请人提供）： 我们建议建立一个新的跨学科的研究培训计划，在计算生物学作为麻省理工学院，怀特黑德研究所和广泛的研究所之间的合作努力。该计划的目标是培养计算机科学专业的学生成为有效的跨学科科学家，与生物学家合作开发新的算法，工具和方法，用于分析实验生物数据，并以原则性预测模型的形式表达这种分析。该计划的教师将包括五名麻省理工学院EECS计算机科学教师，四名麻省理工学院/怀特黑德生物学教师，以及麻省理工学院布罗德研究所的负责人。该计划的主要研究学科包括：开发用于分析生物实验数据的新方法和算法，基于过去数据的生物实验原则设计方法，解释复杂生物现象的计算模型的构建，以及开发用于解释与人类健康和疾病相关的临床数据的方法。 建议在该计划中支持四名博士前学员。我们已经在这方面运行了一个非正式的培训计划超过三年，我们的学生到目前为止已经作出了重大贡献的领域。在我们最近的毕业生是教师在斯坦福大学，普林斯顿大学，杜克，和CMU。我们的申请人库异常强大，2004年有574名申请人在计算机科学的相关子领域。 我们建议的研究培训计划的学员将从麻省理工学院计算机科学研究生课程中获得非常严格的技术和定量基础，结合正式的跨学科课程工作以及计算机科学和生物学教师之间的共同导师安排。我们博士前学生的强大技术技能为创造计算生物学中开创性的新方法和算法提供了良好的基础。此外，我们已经运行场外跨学科的夏季务虚会在过去的三年中，已经非常有效地催化生产的博士前研究。

项目成果

Rapid haplotype inference for nuclear families.

• DOI: 10.1186/gb-2010-11-10-r108
• 发表时间: 2010
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Williams AL;Housman DE;Rinard MC;Gifford DK
• 通讯作者: Gifford DK

Matt: local flexibility aids protein multiple structure alignment.

• DOI: 10.1371/journal.pcbi.0040010
• 发表时间: 2008-01
• 期刊: PLOS COMPUTATIONAL BIOLOGY
• 影响因子: 4.3
• 作者: Menke, Matthew;Berger, Bonnie;Cowen, Lenore
• 通讯作者: Cowen, Lenore