HBCU-RISE: Bridging Quantitative Science with Biological Research: Jumpstarting Computational Systems Biology Research at PVAMU

HBCU-RISE：将定量科学与生物学研究联系起来：在 PVAMU 启动计算系统生物学研究

• 批准号: 1736196
• 负责人: Lijun Qian
• 金额: $ 100万
• 依托单位: Prairie View A & M 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: 2017-09-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736196&HistoricalAwards=false
• 关键词: HBCU; RISE; Bridging; Quantitative; Science

The Historically Black Colleges and Universities Research Infrastructure for Science and Engineering (HBCU-RISE) activity within the Centers of Research Excellence in Science and Technology (CREST) program supports the development of research capabilities at HBCUs that offer doctoral degrees in science and engineering disciplines. HBCU-RISE projects have a direct connection to the long-term plans of the host department(s) and the institutional mission, and plans for expanding institutional research capacity as well as increasing the production of doctoral students in science and engineering. With support from the National Science Foundation, Prairie View A&M University (PVAMU) aims to provide innovative solutions to more effective and efficient drug development by bridging quantitative research with biomedical science. The project aims to 1) jumpstart computational biology research to stimulate students' interest and enhance the PhD program in Electrical Engineering, 2) improve student enrollment and retention, and 3) attract more minority students to pursue graduate study, especially doctoral degrees. This project is aligned with the mission of the institution and the goals of the Electrical and Computer Engineering (ECE) Department. The proposed activities will support the ECE department in building a strong research program in computational biology, thus achieving the goals of enhancing the PhD program in the ECE department and broadening participation in computational biology at PVAMU. The proposed project will greatly improve African American involvement in cutting edge research that is extremely valuable to the nation. The aim of this project is to study and analyze the dynamic evolution of drug/cell interactions using biomedical big data, including both public domain data and dynamic time series data from systematic drug perturbations experiments. Innovative image processing, machine learning, dynamic modeling and control techniques are proposed to help understand the genetic regulation of cancer cells and the mechanism of action of molecularly targeted agents on gene regulation. Specifically, combining the information from robust image feature extraction using advanced image processing techniques (Thrust 1) with candidate drug targets and the identification of drug treatments identified using a novel network-based computational tool, Evaluation of Differential DependencY (EDDY; Thrust 2). Dynamic modeling and analysis of drug response in critical biological pathways will be carried out in Thrust 3. Equipped with the knowledge extracted from biomedical big data obtained in Thrust 2 and a predictive preclinical model that reveal how biological regulatory networks react when perturbed from time series data in Thrust 3, novel therapeutic interventions will be designed in Thrust 4 using advanced control theory. Findings from this study will provide innovative solutions to more effective and efficient drug development by bridging quantitative research with biomedical science. This project will be conducted in collaboration with the TEES-AgriLife Center for Bioinformatics and Genomics Systems Engineering (CBGSE) at Texas A&M University and the Translational Genomic Research Institute (TGen). The knowledge gained from this project will be disseminated broadly to a community of scientists and engineers.

历史上的黑人学院和大学科学与工程研究基础设施（HBCU-RISE）活动在科学和技术卓越研究中心（CREST）计划支持HBCU的研究能力的发展，提供科学和工程学科的博士学位。HBCU-RISE项目与主办部门的长期计划和机构使命以及扩大机构研究能力以及增加科学和工程博士生产量的计划有直接联系。在美国国家科学基金会的支持下，Prairie View AM University（PVAMU）旨在通过将定量研究与生物医学科学相结合，为更有效和更高效的药物开发提供创新解决方案。该项目旨在1）启动计算生物学研究，以激发学生的兴趣，提高电气工程博士课程，2）提高学生入学率和保留率，3）吸引更多的少数民族学生攻读研究生课程，特别是博士学位。该项目与该机构的使命以及电气和计算机工程（ECE）部门的目标保持一致。拟议的活动将支持ECE部门建立一个强大的计算生物学研究计划，从而实现增强ECE部门的博士课程和扩大PVAMU计算生物学参与的目标。拟议的项目将大大提高非洲裔美国人参与尖端研究，这是非常有价值的国家。该项目的目的是利用生物医学大数据研究和分析药物/细胞相互作用的动态演变，包括公共领域数据和来自系统药物扰动实验的动态时间序列数据。提出了创新的图像处理、机器学习、动态建模和控制技术，以帮助理解癌细胞的遗传调控以及分子靶向药物对基因调控的作用机制。具体而言，将使用高级图像处理技术（推力1）进行稳健图像特征提取的信息与候选药物靶标相结合，并使用基于网络的新型计算工具（EDDY;推力2）识别药物治疗。关键生物学途径中药物反应的动态建模和分析将在Thrust 3中进行。配备从Thrust 2中获得的生物医学大数据中提取的知识和预测性临床前模型，揭示生物调控网络在Thrust 3中的时间序列数据扰动时如何反应，Thrust 4将使用先进的控制理论设计新的治疗干预措施。这项研究的结果将通过将定量研究与生物医学科学联系起来，为更有效和更高效的药物开发提供创新的解决方案。该项目将与德克萨斯州A M大学的TEES-AgriLife生物信息学和基因组学系统工程中心（CBGSE）和翻译基因组研究所（TGen）合作进行。从该项目中获得的知识将广泛传播给科学家和工程师社区。

项目成果

Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data

• DOI: 10.1109/tcbb.2022.3173587
• 发表时间: 2023-03-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Dong, Xishuang;Chowdhury, Shanta;Qian, Lijun
• 通讯作者: Qian, Lijun

Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach

• DOI: 10.1109/tbme.2017.2723957
• 发表时间: 2018-04
• 期刊: IEEE Transactions on Biomedical Engineering
• 影响因子: 4.6
• 作者: W. Oduola;Xiangfang Li;C. Duan;Lijun Qian;E. Dougherty

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

• DOI: 10.25046/aj040121
• 发表时间: 2019
• 期刊: Advances in Science, Technology and Engineering Systems Journal
• 作者: S. Bamgbose;Lijun Li

A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records

• DOI: 10.1186/s12859-018-2467-9
• 发表时间: 2018-12-28
• 期刊: BMC BIOINFORMATICS
• 影响因子: 3
• 作者: Chowdhury, Shanta;Dong, Xishuang;Yu, Qiubin
• 通讯作者: Yu, Qiubin

Neural network-based non-linear adaptive controller design for a class of bilinear system

• DOI: 10.1049/ccs.2019.0015
• 发表时间: 2020-02
• 期刊: Cogn. Comput. Syst.
• 作者: S. Bamgbose;Xiangfang Li;Lijun Qian