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229 results on '"Jia, Baolei"'

7. Project-Based Learning in a Collaborative Group Can Enhance Student Skill and Ability in the Biochemical Laboratory: A Case Study

Author

Li, Bing, Jia, Xiaotao, Chi, Yuxia, Liu, Xinli, and Jia, Baolei

Abstract

Experimental courses for undergraduate students majoring in biochemistry or related subjects often do not provide students with systematic and research-based experiences. To help students develop abilities related to laboratory techniques, data analysis, and systematic thought in biology, we performed an exploratory program that employs project-based learning in collaborative groups. The participants (total of 18 students) organized themselves into groups of 2-4 students, and each group researched an enzyme that had not been described previously. The program began with a literature survey of enzyme and bioinformatics analysis. The students cloned the gene encoding the enzyme, purified the enzyme, and, finally, analyzed the enzyme's catalytic characteristics. The students explained the catalytic mechanism, integrating their experimental data and other knowledge. An instructor provided support and training during the process to support effective teamwork and to cultivate a habit of independence that is believed to be useful for the students' future careers. The assessment showed that the pilot program yielded an improvement in the participant' laboratory skills, scientific presentation ability, and experimental design ability. These analyses indicated that the small-scale practice in this study provided benefits to the students and the methods may be popularized to a large extent.

Published
2020
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15. EasyAmplicon: An easy‐to‐use, open‐source, reproducible, and community‐based pipeline for amplicon data analysis in microbiome research

Author

Liu, Yong‐Xin, primary, Chen, Lei, additional, Ma, Tengfei, additional, Li, Xiaofang, additional, Zheng, Maosheng, additional, Zhou, Xin, additional, Chen, Liang, additional, Qian, Xubo, additional, Xi, Jiao, additional, Lu, Hongye, additional, Cao, Huiluo, additional, Ma, Xiaoya, additional, Bian, Bian, additional, Zhang, Pengfan, additional, Wu, Jiqiu, additional, Gan, Ren‐You, additional, Jia, Baolei, additional, Sun, Linyang, additional, Ju, Zhicheng, additional, Gao, Yunyun, additional, Wen, Tao, additional, and Chen, Tong, additional

Published
2023
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40. STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability

Author

Chen, Jinghong, Zhu, Lingxuan, Mou, Weiming, Lin, Anqi, Zeng, Dongqiang, Qi, Chang, Liu, Zaoqu, Jiang, Aimin, Tang, Bufu, Shi, Wenjie, Kahlert, Ulf D., Zhou, Jianguo, Guo, Shipeng, Lu, Xiaofan, Sun, Xu, Ngo, Trunghieu, Pu, Zhongji, Jia, Baolei, Jeon, Che Ok, He, Yongbin, Wu, Haiyang, Gu, Shuqin, Cheungpasitporn, Wisit, Huang, Haojie, Mao, Weipu, Wang, Shixiang, Chen, Xin, Cabannes, Loïc, Ren, Gerald Sng Gui, Whitaker, Iain S., Ali, Stephen, Cheng, Quan, Miao, Kai, Yuan, Shuofeng, and Luo, Peng

Abstract

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32-item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org. This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32-item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts across key aspects including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in this emerging field. This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32-item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts.It consists of key aspects, including question collection, querying approaches, and assessment techniques.It enhances research quality and facilitates advances in this emerging field. This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32-item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts. It consists of key aspects, including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in this emerging field.

Published
2024
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