CAREER: Machine Learning Approaches for Genome-wide Biological Network Inference

职业：全基因组生物网络推理的机器学习方法

• 批准号: 1347706
• 负责人: Xue-Wen Chen
• 金额: $ 8.52万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-11-15 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1347706&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Approaches; Genome

NSF-0644366Chen, Xue-WenThe objectives of this research program are (1) to develop and apply novel computationalapproaches for uncovering genome-wide networks of interactions between genes and proteins, and (2) to conduct related educational activities in a newly established bioinformatics program in the Department of Electrical Engineering and Computer Science at the University of Kansas. Specifically, built upon reconstructing biological networks of moderate size, the new research will computationally uncover genome-wide biological networks and map interactions of genes and proteins across a variety of organisms. The research directions include: Simultaneously integrating multiple biological knowledge into dynamic Bayesian networks for learning networks of gene interactions; learning networks of protein interactions from heterogeneous data; learning integrated networks of gene and protein interactions; learning genome-wide networks of gene and protein interactions; and cross-species network learning. It will advance the state of the art by developing machine learning methods for effectively integrating multiple prior knowledge from different sources of data, including learning for highly heterogeneous data and large-scale network. The research will also produce new methods and user-friendly software that can be applied by molecular biologists to gain insight into diverse biological problems, such as how biological processes are regulated on a genome scale and how individual bio-molecules interact with one another in the cell.Learning with prior knowledge and highly heterogeneous data sources are fundamental to computational biology, information theory, machine learning, data mining, and other areas. Thus, the proposed research will benefit a variety of application domains including research in biology and medicine. The biological discovery derived from this project will also contribute to a variety of fields that include agriculture development, rational drug design, and health care. The research program will foster and facilitate collaborations between biologists and the PI. The educational components are closely tied to the research activities, which include (1) developing and improving bioinformatics courses that are closely related to the research outlined here and integrating them into the core bioinformatics curriculum, and (2) providing special training opportunities in the interdisciplinary area of bioinformatics for a wide-range of students, from high school through graduate school, including groups typically underrepresented in the field of science and technology.

NSF-0644366陈学文该研究项目的目标是（1）开发和应用新的计算方法来揭示基因和蛋白质之间相互作用的全基因组网络，以及（2）在新建立的生物信息学项目中开展相关的教育活动在堪萨斯大学电气工程和计算机科学系。具体来说，基于重建中等规模的生物网络，这项新研究将通过计算揭示全基因组生物网络，并绘制各种生物体中基因和蛋白质的相互作用图谱。研究方向包括：将多种生物知识同时整合到动态贝叶斯网络中，用于学习基因相互作用的网络；从异构数据中学习蛋白质相互作用的网络；学习基因和蛋白质相互作用的集成网络；学习基因和蛋白质相互作用的全基因组网络；和跨物种网络学习。它将通过开发有效集成来自不同数据源的多种先验知识的机器学习方法来推进最先进的技术，包括高度异构数据和大规模网络的学习。该研究还将产生新的方法和用户友好的软件，分子生物学家可以应用这些方法和用户友好的软件来深入了解不同的生物学问题，例如如何在基因组规模上调节生物过程以及单个生物分子如何在基因组中彼此相互作用。利用先验知识和高度异构的数据源进行学习是计算生物学、信息论、机器学习、数据挖掘和其他领域的基础。因此，拟议的研究将有益于各种应用领域，包括生物学和医学研究。该项目的生物学发现也将为农业发展、合理药物设计和医疗保健等多个领域做出贡献。该研究计划将促进和促进生物学家和 PI 之间的合作。教育组成部分与研究活动密切相关，其中包括（1）开发和改进与此处概述的研究密切相关的生物信息学课程，并将其整合到核心生物信息学课程中，以及（2）提供跨学科的特殊培训机会面向从高中到研究生的广大学生的生物信息学领域，包括在科学和技术领域代表性不足的群体。