Zylla Jessica Ls, Gnimpieba Z Etienne, Bomgni Alain B, Sani Rajesh K, Subramaniam Mahadevan, Lushbough Carol, Winter Robb, Gadhamshetty Venkataramana R, Chundi Parvathi
Res Sq. 2023 Sep 8:rs.3.rs-3318640. doi: 10.21203/rs.3.rs-3318640/v1.
Initially, research disciplines operated independently, but the emergence of trans-disciplinary sciences led to convergence research, impacting graduate programs and research laboratories, especially in bioengineering and material engineering as presented here. Current graduate curriculum fails to efficiently prepare students for multidisciplinary and convergence research, thus creating a gap between the students and research laboratory expectations. We present a convergence training framework for graduate students, incorporating problem-based learning under the guidance of senior scientists and collaboration with postdoctoral researchers. This case study serves as a template for transdisciplinary convergent training projects - bridging the expertise gap and fostering successful convergence learning experiences in computational biointerface (material-biology interface). The 18-month Advanced Data Science Workshop, initiated in 2019, involves project-based learning, online training modules, and data collection. A pilot solution utilized Jupyter notebook on Google collaborator and culminated in a face-to-face workshop where project presentations and finalization occurred. The program started with 9 experts in the four diverse fields creating 14 curated projects in data science (Artificial Intelligence/Machine Learning), material science, biofilm engineering, and biointerface. These were integrated into convergence research through webinars by the experts. The experts chose 8 of the 14 projects to be part of an all-day in-person workshop, where over 20 learners formed eight teams that tackled complex problems at the interface of digital image processing, gene expression analysis, and material prediction. Each team was comprised of students and postdoctoral researchers or research scientists from diverse domains including computer science, materials science, and biofilm research. Some projects were selected for presentation at the international IEEE Bioinformatics conference in 2022, with three resulting Machine Learning (ML) models submitted as a journal paper. Students engaged in problem discussions, collaborated with experts from different disciplines, and received guidance in decomposing learning objectives. Based on learner feedback, this successful experience allows for consolidation and integration of convergence research via problem-based learning into the curriculum. Three bioengineering participants, who received training in data science and engineering, have received bioinformatics jobs in biotechnology industries.
最初，各研究学科独立运作，但跨学科科学的出现导致了融合研究，影响了研究生项目和研究实验室，尤其是本文所介绍的生物工程和材料工程领域。当前的研究生课程未能有效地让学生为多学科和融合研究做好准备，从而在学生期望与研究实验室期望之间造成了差距。我们为研究生提出了一个融合培训框架，包括在资深科学家的指导下开展基于问题的学习以及与博士后研究人员合作。本案例研究作为跨学科融合培训项目的模板——弥合专业知识差距，并在计算生物界面（材料 - 生物学界面）培养成功的融合学习体验。2019年启动的为期18个月的高级数据科学研讨会，涉及基于项目的学习、在线培训模块和数据收集。一个试点解决方案在谷歌协作平台上使用了Jupyter笔记本，并最终以面对面研讨会的形式结束，在该研讨会上进行项目展示和定稿。该项目最初有来自四个不同领域的9位专家，他们创建了14个数据科学（人工智能/机器学习）、材料科学、生物膜工程和生物界面方面的精选项目。这些项目通过专家的网络研讨会被整合到融合研究中。专家们从14个项目中挑选了8个作为全天面对面研讨会的一部分，在该研讨会上，20多名学习者组成了八个团队，解决数字图像处理、基因表达分析和材料预测界面的复杂问题。每个团队由来自不同领域（包括计算机科学、材料科学和生物膜研究）的学生和博士后研究人员或研究科学家组成。一些项目被选中在2022年的国际电气和电子工程师协会（IEEE）生物信息学会议上展示，其中三个生成的机器学习（ML）模型作为期刊论文提交。学生们参与问题讨论，与来自不同学科的专家合作，并在分解学习目标方面获得指导。基于学习者的反馈，这一成功经验使得通过基于问题的学习将融合研究巩固和整合到课程中成为可能。三名接受过数据科学和工程培训的生物工程参与者，已在生物技术行业获得了生物信息学相关工作。
