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

本研究项目的目标是：(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；(2)在堪萨斯大学电子工程与计算机科学系新成立的生物信息学项目中开展相关的教育活动。具体来说，在重建中等规模的生物网络的基础上，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，构建基因相互作用学习网络；从异构数据中学习蛋白质相互作用网络；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；以及跨物种网络学习。它将通过开发机器学习方法来有效地集成来自不同数据源的多个先验知识，包括高度异构数据和大规模网络的学习，从而推进当前的技术水平。这项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软件来深入了解各种生物问题，例如生物过程如何在基因组规模上受到调节，以及单个生物分子如何在细胞中相互作用。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，所提出的研究将有利于各种应用领域，包括生物学和医学研究。从这个项目中获得的生物学发现也将有助于包括农业发展、合理药物设计和卫生保健在内的各种领域。该研究项目将促进和促进生物学家与PI之间的合作。教育组成部分与研究活动密切相关，包括(1)开发和改进与这里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；(2)为从高中到研究生院的广泛学生提供生物信息学跨学科领域的特殊培训机会。包括在科学技术领域代表性不足的群体。

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.