D3SC: EAGER: Data-driven development of fluorescent sensors for bio-imaging

D3SC：EAGER：生物成像荧光传感器的数据驱动开发

• 批准号: 1738305
• 负责人: Ming Xian
• 金额: $ 30万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738305&HistoricalAwards=false
• 关键词: D3SC; EAGER; Data; driven; development

Chemical information is growing dramatically, fueled by the massive amount of data generated by researchers in a variety of fields. Conventional approaches require that scientists sift through data from many sources to try to develop a comprehensive picture of the field when making a new chemical compound or studying a chemical processes. This conventional approach is slow and difficult as researchers can miss important trends. With the support from the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry and the Cyberinfrastructure for Emerging Science and Engineering Research (CESER) Program in the Office of Advanced Cyberinfrastructure, Professor Xian at Washington State University (WSU) and Professor Ji are teaching machines to collect massive data from literature, analyze them, and come up with new design principles to make new sensors. The project is in response to the Data-Driven Discovery Science in Chemistry Dear Colleague Letter (D3SC-DCL). The team is using data mining and computer learning to develop a new generation of sensors for the detection of hydrogen sulfide (H2S). Hydrogen sulfide is an important signaling molecule that is associated with biological processes such as high blood pressure, atherosclerosis, coronary heart diseases. The coupling of computer science techniques with chemical problems enables Professors Xian and Ji and their students to predict the most promising sensor design without making and testing a large number of sensors empirically. The research group "trains their computer" by testing the predicted sensors designs and providing feedback for the next iteration; this is machine learning. If successful, the machine learning methods may be expanded to sensor development for the detection of many different compounds, especially those that are important in biological and chemical processes (such as biological warfare and pharmaceutical development). The research provides unique training opportunities for undergraduate and graduate students by providing rich experiences in both chemistry and data science. These activities build the workforce of non-traditional chemistry trainees to meet data-driven research and development needs in industry and academia. Professor Xian also actively works with undergraduate students from underrepresented minority groups by participating the Pacific Northwest Louis Stokes Alliance for Minority Participation (PNW-LSAMP) Program at WSU.Professors Xian and Ji are building a database of H2S sensors with searchable parameters. They are carrying out data-driven optimization of the sensors based on advanced machine learning and data mining techniques. A combined data-driven discovery framework of unsupervised learning and supervised multi-task learning is developed to predict the important properties of the sensors. This approach is used to identify the most suitable fluorophores and H2S-reaction sites for the design of optimal sensors, which can then be synthesized and validated. These studies may advance our understanding of machine learning and data mining as well as chemical predication and sensor development. This interdisciplinary project provides a unique platform to attract students to chemistry and train them at the interface of chemistry, data science, and computation science. Broadening participation efforts include students from underrepresented minority groups through the Pacific Northwest Louis Stokes Alliance for Minority Participation (PNW-LSAMP) Program at WSU.

在各个领域的研究人员产生的大量数据的推动下，化学信息正在急剧增长。传统的方法要求科学家在制造一种新的化合物或研究一种化学过程时，从许多来源筛选数据，试图对该领域形成一个全面的了解。这种传统的方法既缓慢又困难，因为研究人员可能会错过重要的趋势。在化学系化学测量与成像（CMI）项目和先进网络基础设施办公室新兴科学与工程研究网络基础设施（CESER）项目的支持下，华盛顿州立大学（WSU）的冼教授和纪教授正在教授机器从文献中收集大量数据，对其进行分析，并提出新的设计原则来制造新的传感器。该项目是响应数据驱动发现科学在化学Dear Colleague Letter （D3SC-DCL）。该团队正在利用数据挖掘和计算机学习技术开发新一代硫化氢（H2S）检测传感器。硫化氢是一种重要的信号分子，与高血压、动脉粥样硬化、冠心病等生物过程有关。计算机科学技术与化学问题的结合，使Xian教授和Ji教授及其学生能够预测最有前途的传感器设计，而无需制作和测试大量的经验传感器。研究小组通过测试预测的传感器设计并为下一次迭代提供反馈来“训练他们的计算机”；这就是机器学习。如果成功，机器学习方法可以扩展到用于检测许多不同化合物的传感器开发，特别是那些在生物和化学过程中很重要的化合物（如生物战和药物开发）。该研究提供了丰富的化学和数据科学经验，为本科生和研究生提供了独特的培训机会。这些活动建立了非传统化学受训人员队伍，以满足工业界和学术界数据驱动的研究和开发需求。西安教授还积极参与华盛顿州立大学太平洋西北路易斯·斯托克斯少数民族参与联盟（PNW-LSAMP）项目，与来自代表性不足的少数民族的本科生合作。Xian教授和Ji教授正在建立一个具有可搜索参数的H2S传感器数据库。他们正在基于先进的机器学习和数据挖掘技术对传感器进行数据驱动优化。提出了一种结合无监督学习和监督多任务学习的数据驱动发现框架，用于预测传感器的重要特性。该方法用于确定最合适的荧光团和h2s反应位点，用于设计最佳传感器，然后可以合成和验证。这些研究可能会促进我们对机器学习和数据挖掘以及化学预测和传感器开发的理解。这个跨学科的项目提供了一个独特的平台，吸引学生学习化学，并在化学，数据科学和计算科学的界面上训练他们。扩大参与的努力包括通过华盛顿州立大学的太平洋西北路易斯·斯托克斯少数民族参与联盟（PNW-LSAMP）项目，来自代表性不足的少数群体的学生。