FET: CCF: Small: Computational Drug Prediction through Joint Learning

FET：CCF：小型：通过联合学习进行计算药物预测

• 批准号: 2006780
• 负责人: Jing Li
• 金额: $ 26万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006780&HistoricalAwards=false
• 关键词: FET; CCF; Small; Computational; Drug

Traditional target-based drug-development approaches are lengthy and costly, with high failure rates. With increasingly available data regarding drugs, diseases, and drug targets, computational approaches that can integrate diverse sets of heterogeneous data sources have great potential to speed up the drug-development process. This project addresses fundamental questions in computational drug prediction by developing advanced AI and machine-learning algorithms and applying them to comprehensive real data from different domains. The project will also support broadening participation in computing via educational and outreach activities geared towards a wide group of students, including underrepresented groups.More specifically, the project will study the relationships among drugs, diseases, and targets via powerful computational methods including tensors and tensor decomposition, multi-view learning, and deep learning for rational drug repurposing. The project will focus on the development of efficient and effective learning algorithms, rigorous theoretical analysis of algorithm convergence and complexity, and comprehensive evaluations using real data obtained from various databases. The approach will seamlessly integrate tensor decomposition with multi-view learning and deep learning. By utilizing auxiliary information in the framework of multi-view learning, it can effectively address the sparsity issue, and the learned hidden structures should be more meaningful and interpretable. The neural tensor-decomposition approach will allow exploration of more complicated nonlinear relationships among latent features.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.

传统的以靶向为基础的药物开发方法费时费钱，失败率高。随着越来越多的关于药物、疾病和药物靶点的数据可用，可以集成各种不同类型数据源的计算方法在加快药物开发过程方面具有巨大的潜力。该项目通过开发先进的人工智能和机器学习算法，并将它们应用于来自不同领域的综合真实数据，解决了计算药物预测中的基本问题。该项目还将通过面向包括代表不足的群体在内的广泛学生群体的教育和外展活动，支持扩大对计算的参与。更具体地说，该项目将通过强大的计算方法研究药物、疾病和目标之间的关系，包括张量和张量分解、多视角学习和理性药物再利用的深度学习。该项目将侧重于开发高效和有效的学习算法，对算法的收敛和复杂性进行严格的理论分析，并利用从各种数据库获得的真实数据进行综合评估。该方法将张量分解与多视角学习和深度学习无缝结合。通过在多视点学习框架中利用辅助信息，可以有效地解决稀疏性问题，学习到的隐含结构应该更有意义和可解释性。神经张量分解方法将允许探索潜在特征之间更复杂的非线性关系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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

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