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

AF：小：同质和异构网络学习及其在计算生物学中的应用

• 批准号: 1815139
• 负责人: Jing Li
• 金额: $ 20万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815139&HistoricalAwards=false
• 关键词: AF; Small; Homogeneous; Heterogeneous; Network

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.

异构网络已被广泛应用于现实世界的复杂系统建模，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，异构网络中高效的链路预测算法的研究仍处于起步阶段，特别是对于集成多个数据源的网络。本项目的总体目标是为异构网络中的链接预测问题开发高效、鲁棒、有效和集成的算法和软件工具，并将算法应用于真实的生物应用中，具有实际意义。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接预测问题：一种是基于核正则化最小二乘框架的数据不完备多视图学习，另一种是利用加权非负矩阵三因子分解方法，该方法为链路预测和数据填补提供了统一的框架。这两种方法都将被应用到药物组合预测的问题，这是制定为异构网络中的链接预测问题。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems

• DOI: 10.24963/ijcai.2020/339
• 发表时间: 2020-07
• 作者: Huiyuan Chen;Jing Li

Finding Stable Clustering for Noisy Data via Structure-Aware Representation

• DOI: 10.1109/bigdata47090.2019.9006431
• 发表时间: 2019-12
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Huiyuan Chen;Jing Li

Collective Tensor Completion with Multiple Heterogeneous Side Information

• DOI: 10.1109/bigdata47090.2019.9006072
• 发表时间: 2019-12
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Huiyuan Chen;Jing Li

iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding

• DOI: 10.1371/journal.pcbi.1008040
• 发表时间: 2020-07-01
• 期刊: PLOS COMPUTATIONAL BIOLOGY
• 影响因子: 4.3
• 作者: Chen, Huiyuan;Cheng, Feixiong;Li, Jing
• 通讯作者: Li, Jing

Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network

通过神经张量网络学习数据驱动的药物-靶标-疾病相互作用

• DOI: 10.24963/ijcai.2020/477
• 发表时间: 2020
• 期刊: International Joint Conference on Artificial Intelligence (IJCAI
• 作者: Chen, Huiyuan;Li, Jing
• 通讯作者: Li, Jing