The overall goal of the proposed Fisk- UIUC KnowEnG R25 program is to recruit and retain a cadre of under-represented minority scientists prepared to compete for PhD training in biomedical research with already acquired confidence in the use of Big Data. The proposed partnership with the KnowEnG BD2K Center at UIUC will permit curricular enhancements and summer research opportunities for Fisk trainees while, at the same time, reciprocally training natural scientists and mathematics majors in complementary computer and informatics sciences and providing computer science and mathematics undergraduates with essential systems, molecular and cell biology/biochemistry background at Fisk University to provide context for cutting edge genomics, proteomics, and individualized medicine research reliant on Big Data. In addition to curricular and research training program elements, Fisk students will have remote access to seminar courses to increase efficacy in communicating BD2K-based technologies and their applications. Didactic work and undergraduate research experiences will be complemented by an individualized student development plan for honing professional skills, deep understanding of the responsible conduct of research, and wrap-around mentoring to assure subsequent successful entry into competitive BD2K aligned PhD-granting programs. UIUC-hosted summer workshops for faculty will increase confidence in use of Big Data tools, leading to innovations in STEM courses that embrace Big Data, impacting all Fisk STEM undergraduates. Research collaborations between Fisk and BD2K partner faculty also will be fostered.
The aims to achieve the goals are: 1) Implement an integrated didactic program to enhance student self-efficacy with computational and informatics tool development and use for interrogating and interpreting Big Data, including a two-semester bioinformatics course in Bioinformatics, informed by the expertise of UIUC KnowEnG BD2K Center faculty with additional Special Topics courses available remotely from UIUC. 2) Develop an integrated academic year (Fisk, or partners) and summer research program at the UIUC KnowEnG BD2K Center to assure student exposure to a participation in the life cycle of a `Big Data' research problem. 3) Implement a professional skills development program that assures successful transition of undergraduate participants to a Ph.D. (or MD/PhD Program) in Big Data- reliant biomedical research. 4) Launch a faculty development program in bioinformatics that leads to embracing Big Data problems in courses in multiple disciplines for impact on all Fisk undergraduate STEM majors. The proposed program will increase both didactic and research experiences in Big Data for Fisk University undergraduates while preparing them for successful entry into PhD-granting programs in related disciplines at research intensive universities. Our KnowEnG partnership also will increase Fisk faculty capacity in Big Data use and foster faculty research collaborations, thus introducing Big Data into course-embedded research, impacting all Fisk University STEM Majors. Reciprocally, our KnowEnG UIUC faculty partners will enrich their holistic mentoring skills of URM trainees based on interactions with Fisk R25 mentors, of value for their broader education and research training goals at UIUC and Mayo.
The overall goal of the proposed Fisk- UIUC KnowEnG R25 program is to recruit and retain a cadre of under-represented minority scientists prepared to compete for PhD training in biomedical research with already acquired confidence in the use of Big Data. The proposed partnership with the KnowEnG BD2K Center at UIUC will permit curricular enhancements and summer research opportunities for Fisk trainees while, at the same time, reciprocally training natural scientists and mathematics majors in complementary computer and informatics sciences and providing computer science and mathematics undergraduates with essential systems, molecular and cell biology/biochemistry background at Fisk University to provide context for cutting edge genomics, proteomics, and individualized medicine research reliant on Big Data. In addition to curricular and research training program elements, Fisk students will have remote access to seminar courses to increase efficacy in communicating BD2K-based technologies and their applications. Didactic work and undergraduate research experiences will be complemented by an individualized student development plan for honing professional skills, deep understanding of the responsible conduct of research, and wrap-around mentoring to assure subsequent successful entry into competitive BD2K aligned PhD-granting programs. UIUC-hosted summer workshops for faculty will increase confidence in use of Big Data tools, leading to innovations in STEM courses that embrace Big Data, impacting all Fisk STEM undergraduates. Research collaborations between Fisk and BD2K partner faculty also will be fostered. The proposed program will increase both didactic and research experiences in Big Data for Fisk University undergraduates while preparing them for successful entry into PhD-granting programs in related disciplines at research intensive universities. Our KnowEnG partnership also will increase Fisk faculty capacity in Big Data use and foster faculty research collaborations, thus introducing Big Data into course-embedded research, impacting all Fisk University STEM Majors. Reciprocally, our KnowEnG UIUC faculty partners will enrich their holistic mentoring skills of URM trainees based on interactions with Fis R25 mentors, of value for their broader education and research training goals at UIUC and Mayo.
| Kim, Minji; Kim, Yeonsung; Qian, Lei et al. (2017) TeachEnG: a Teaching Engine for Genomics. Bioinformatics 33:3296-3298 |
| Canner, Judith E; McEligot, Archana J; Pérez, María-Eglée et al. (2017) Enhancing Diversity in Biomedical Data Science. Ethn Dis 27:107-116 |
| Yaseen, Ashraf; Nijim, Mais; Williams, Brandon et al. (2016) FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information. BMC Bioinformatics 17 Suppl 8:281 |
