Spokes: MEDIUM: SOUTH: Collaborative: Integrating Biological Big Data Research into Student Training and Education

辐条：中：南：协作：将生物大数据研究融入学生培训和教育

基本信息

批准号：
1761839
负责人：
Hong Qin
金额：
$ 54.99万
依托单位：
University of Tennessee Chattanooga
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：
2018-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761839&HistoricalAwards=false
关键词：
Spokes MEDIUM SOUTH Collaborative Integrating

项目摘要

The project is a collaborative effort among the University of Tennessee Chattanooga, Tuskegee University, Spelman College, and West Virginia University to integrate and automate biological big data into student training and education. Leveraging the team's expertise in computer science and ecology, the project will offer training workshops on using network models to integrate heterogeneous genomic big data and heterogeneous ecological big data to address life sciences questions. The team will engage faculty and students in developing a protocol to automate field data collection. The team also will prototype automated methods to enhance plant digitization, leveraging the collection of digitized plant images and meta-information at the Southeast Regional Network of Expertise and Collections, as well as the ecological datasets in collaboration with the Encyclopedia of Life.The project objectives are to (1) enhance faculty expertise in big biological data through summer workshops; (2) catalyze interdisciplinary collaboration on big biological data research and education through hackathons, working groups, and community-building via a Video Education Faculty Network; and (3) develop hands-on, constructively peer-evaluated learning modules incorporating high-quality video tutorials. The proposed activities will address challenges surrounding the integration and automation of big biological data into education and training at predominantly undergraduate institutions and Historically Black Colleges and Universities. The project will help bridge the gaps between big biological data and the fields of systems biology, ecology and evolution, and environmental sciences. Overall, the project will catalyze collaborations among diverse institutions and disciplines while increasing diversity in big data. This award is co-funded by the Improving Undergraduate STEM Education: Education and Human Resources (IUSE): EHR Program (NSF 17-590). IUSE supports projects that are designed to improve student learning through development of new curricular materials and methods of instruction and development of new assessment tools to measure student learning.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.

该项目是田纳西大学查塔努加分校、塔斯基吉大学、斯佩尔曼学院和西弗吉尼亚大学之间的合作项目，旨在将生物大数据集成并自动化到学生培训和教育中。利用该团队在计算机科学和生态学方面的专业知识，该项目将提供有关使用网络模型整合异构基因组大数据和异构生态大数据以解决生命科学问题的培训研讨会。该团队将让教师和学生参与开发自动化现场数据收集的协议。该团队还将利用东南地区专业知识和收藏网络收集的数字化植物图像和元信息，以及与生命百科全书合作的生态数据集，建立自动化方法的原型，以增强植物数字化。项目目标(1) 通过夏季研讨会增强教师在生物大数据方面的专业知识； (2) 通过黑客马拉松、工作组和视频教育教师网络社区建设，促进生物大数据研究和教育的跨学科合作； (3) 开发结合高质量视频教程的实践性、建设性的同行评估学习模块。拟议的活动将解决围绕将大生物数据集成和自动化纳入以本科院校和传统黑人学院和大学为主的教育和培训的挑战。该项目将有助于弥合生物大数据与系统生物学、生态学和进化以及环境科学领域之间的差距。总体而言，该项目将促进不同机构和学科之间的合作，同时增加大数据的多样性。该奖项由改善本科 STEM 教育：教育和人力资源 (IUSE)：EHR 计划 (NSF 17-590) 共同资助。 IUSE 支持旨在通过开发新的课程材料和教学方法以及开发新的评估工具来衡量学生学习来提高学生学习的项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的知识进行评估，被认为值得支持。优点和更广泛的影响审查标准。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

从核苷酸碱基固定模式中识别判别特征及其在基本基因分类中的应用

DOI：
10.3389/fgene.2023.1154120
发表时间：
2023
期刊：
Frontiers in Genetics
影响因子：
3.7
作者：
Rout, Ranjeet Kumar;Umer, Saiyed;Khandelwal, Monika;Pati, Smitarani;Mallik, Saurav;Balabantaray, Bunil Kumar;Qin, Hong
通讯作者：
Qin, Hong

Applying for, Operating, and Renewing a Research Experience for Undergraduates Site Program in Computational Biology

申请、运营和更新计算生物学本科生研究经验现场项目

DOI：
10.54808/imcic2022.01.171
发表时间：
2022-03
期刊：
Informatics and Cybernetics
影响因子：
0
作者：
Qin; Hong
通讯作者：
Hong

A Comparison of YOLO and Mask-RCNN for Detecting Cells from Microfluidic Images

YOLO 和 Mask-RCNN 在微流控图像中检测细胞的比较

DOI：
10.1109/icaiic54071.2022.9722616
发表时间：
2022-02
期刊：
2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC
影响因子：
0
作者：
Ghafari, Mehran;Mailman, Daniel;Hatami, Parisa;Peyton, Trevor;Yang, Li;Dang, Weiwei;Qin, Hong
通讯作者：
Qin, Hong

Extensive Huffman-tree-based Neural Network for the Imbalanced Dataset and Its Application in Accent Recognition

不平衡数据集的扩展哈夫曼树神经网络及其在口音识别中的应用

DOI：
10.1109/icaiic51459.2021.9415243
发表时间：
2021-04
期刊：
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC
影响因子：
0
作者：
Merrill, Jeremy;Liang, Yu;Wu, Dalei
通讯作者：
Wu, Dalei

Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends

使用物理引导图注意力网络的分层集成来预测 COVID-19 趋势