Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design

合作研究：III：媒介：新的机器学习赋能纳米信息学系统，促进纳米材料设计

• 批准号: 2211492
• 负责人: Heng Huang
• 金额: $ 25.02万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211492&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; New

The research objective of this proposal is to address the computational challenges in the innovative nanomaterial data analysis or nanoinformatics for predicting nanomaterials properties. Nanomaterials are very small materials that can be used in a variety of applications, including nanomedicine development. The vast quantities of existing experimental data require new nanoinformatics approaches and toolkits for data extraction, analysis, and sharing. This can help guide the safe design of next-generation of nanomedicines with desirable therapeutic activities, while also ensuring they have limited side effects. However, there are currently two critical limitations to using machine learning approaches in nanoinformatics modeling studies. First, most existing data available for modeling were based on a limited number of nanomaterials that also have limited experimental characterization of their chemical properties. Second, despite significant efforts from various researchers, the available modeling approaches that have been developed are applicable only for a specified small set of nanomaterials and have rarely been used to design nanomaterials. This project will address the computational challenges in large-scale nanomaterial data mining, development and validation of an automated informatics framework to digitalize nanostructures, identify molecular markers, and support fast nanomaterial retrieval and integrative analysis. This project will also facilitate the development of novel educational tools to enhance several current courses at Rutgers University, University of Pittsburgh, and University of Minnesota. The investigators will engage the minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research.In this project, a novel machine learning based nanoinformatics framework will be developed to integrate new digital nanostructure representations with the emerging key computational techniques. The project focuses on designing principled machine learning and data science algorithms for analyzing large-scale nanomaterial data to create new informatics toolkits to facilitate the nanomedicine-based treatments and new nanomaterial design. Specifically, the following research goals will be met in this project: 1) new computational tools to automate nanostructure digitalization; 2) interpretation method to enhance deep learning based predictive models; 3) new cross-modal deep hashing network for fast and accurate nanomaterial data retrieval; and 4) evaluate the proposed methods and system using real large-scale nanomaterial data and release the database and nanoinformatics toolkits to the public. Unlike most existing nanoinformatics strategies that perform modeling and analysis at a small scale, this project will provide promising new directions to the analysis of large-scale complex nanomaterial data by addressing the critical data-intensive analysis issues including efficiency, scalability, and interpretability. The investigations combine rigorous theoretical analysis and emerging application studies and will contribute to both academic research and potential commercialized products. This project will advance and thus extend the relationship between engineering innovation and computational analysis, and hold great promise for nanomaterial and nanomedicine developments.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.

该建议的研究目标是解决用于预测纳米材料特性的创新纳米材料数据分析或纳米信息学的计算挑战。纳米材料是非常小的材料，可用于包括纳米医学开发在内的各种应用中。大量现有的实验数据需要新的纳米信息方法和工具包，以进行数据提取，分析和共享。这可以帮助指导下一代纳米药物的安全设计，并确保它们的副作用有限。但是，目前在纳米信息建模研究中使用机器学习方法有两个关键局限性。首先，用于建模的大多数现有数据是基于数量有限的纳米材料，这些纳米材料对其化学性质的实验表征也有限。其次，尽管各种研究人员的重大努力，但开发的可用建模方法仅适用于指定的少量纳米材料，并且很少用于设计纳米材料。该项目将解决大规模纳米材料数据挖掘，开发和验证自动化信息学框架的计算挑战，以数字化纳米结构，识别分子标记并支持快速的纳米材料检索和整合分析。该项目还将促进开发新颖的教育工具，以增强罗格斯大学，匹兹堡大学和明尼苏达大学的几门课程。研究人员将与少数族裔学生和服务不足的人群参与研究活动，以使他们更好地接触尖端的科学研究。在该项目中，将开发基于机器学习的新型纳米信息框架，以将新的数字纳米结构表示与新兴的关键计算技术整合在一起。该项目着重于设计原则性的机器学习和数据科学算法，用于分析大型纳米材料数据，以创建新的信息学工具包，以促进基于纳米医学的治疗方法和新的纳米材料设计。具体而言，该项目将实现以下研究目标：1）自动化纳米结构数字化的新计算工具； 2）增强基于深度学习的预测模型的解释方法； 3）新的跨模式深层哈希网络，用于快速，准确的纳米材料数据检索； 4）使用真实的大规模纳米材料数据评估所提出的方法和系统，并将数据库和纳米信息学工具包释放给公众。与大多数在小规模执行建模和分析的纳米信息学策略不同，该项目将通过解决关键的数据密集型分析问题，包括效率，可伸缩性和可解释性，为大规模复杂的纳米材料数据分析提供有希望的新方向。研究结合了严格的理论分析和新兴的应用研究，并将有助于学术研究和潜在的商业化产品。该项目将推进工程创新和计算分析之间的关系，并对纳米材料和纳米医学的发展保持着巨大的希望。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。

项目成果

Toward Unified Data and Algorithm Fairness via Adversarial Data Augmentation and Adaptive Model Fine-tuning

• DOI: 10.1109/icdm54844.2022.00174
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Yanfu Zhang;Runxue Bao;Jian Pei;Heng Huang

Fast Stochastic Recursive Momentum Methods for Imbalanced Data Mining

• DOI: 10.1109/icdm54844.2022.00068
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Xidong Wu;Feihu Huang;Heng Huang

Integrating structure annotation and machine learning approaches to develop graphene toxicity models. 2023

整合结构注释和机器学习方法来开发石墨烯毒性模型。

• 发表时间: 2023
• 期刊: Carbon
• 影响因子: 10.9
• 作者: Wang, T;Russo, D;Bitounis, D;Demokritou, P;Jia, X;Huang, H;Zhu, H
• 通讯作者: Zhu, H