AF: Small: Homogeneous and Heterogeneous Network Learning with Applications in Computational Biology

异构网络已广泛应用于现实世界复杂系统的建模，并已成为研究复杂生物问题的有力工具。异构网络中的链路预测一直是这方面的关键计算问题之一。然而，对于异构网络中高效的链路预测算法的研究还处于起步阶段，特别是对于集成了多个数据源的网络。本课题的总体目标是为异构网络中的链路预测问题开发高效、鲁棒、有效、集成的算法和软件工具，并将算法应用于具有实际意义的实际生物应用。该项目还将有助于促进新一代计算生物学家的教学和培训。该项目的研究人员将通过提出两种计算方法来解决异构网络中的链路预测问题：一种是基于核正则化最小二乘框架的数据不完全性多视图学习，另一种是利用加权非负矩阵三因子分解方法，为链路预测和数据输入提供统一的框架。这两种方法都将应用于药物联合预测问题，该问题被表述为异构网络中的链接预测问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Heterogeneous networks have been widely used in modeling real-world complex systems and have been a powerful tool in studying complex biological problems. Link prediction in heterogeneous networks has been one of the key computational problems in this context. However, research on efficient and effective algorithms for link prediction in heterogeneous networks is still in its infancy, especially for networks that integrate multiple data sources. The overall objective of this project is to develop efficient, robust, effective and integrative algorithms as well as software tools for the link prediction problem in heterogeneous networks, and to apply the algorithms on real biological applications with practical significance. The project will also help in promoting teaching and training of a new generation of computational biologists.The investigators of the project will solve the link prediction problem in heterogeneous networks by proposing two computational approaches: one is based on a kernel regularized least square framework with multi-view learning for data incompleteness, and the other one is to utilize a weighted nonnegative matrix tri-factorization approach that provides a unified framework for link prediction and data imputation. Both approaches will be applied to the problem of drug combination prediction, which is formulated as a link prediction problem in heterogeneous networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.