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Full-Text Articles in Education
The Bioinformatics Virtual Coordination Network: An Open-Source And Interactive Learning Environment, Benjamin J. Tully, Joy Buongiorno, Ashley B. Cohen, Jacob A. Cram, Arkadiy I. Garber, Sarah K. Hu, Arianna I. Krinos, Philip T. Leftwich, Alexis J. Marshall, Ella T. Sieradzki, Daan R. Speth, Elizabeth A. Suter, Christopher B. Trivedi, Luis E. Valentin-Alvarado, Jake L. Weissman, Bvcn Instructor Consortium
The Bioinformatics Virtual Coordination Network: An Open-Source And Interactive Learning Environment, Benjamin J. Tully, Joy Buongiorno, Ashley B. Cohen, Jacob A. Cram, Arkadiy I. Garber, Sarah K. Hu, Arianna I. Krinos, Philip T. Leftwich, Alexis J. Marshall, Ella T. Sieradzki, Daan R. Speth, Elizabeth A. Suter, Christopher B. Trivedi, Luis E. Valentin-Alvarado, Jake L. Weissman, Bvcn Instructor Consortium
Faculty Works: Biology, Chemistry, and Environmental Studies
Lockdowns and “stay-at-home” orders, starting in March 2020, shuttered bench and field dependent research across the world as a consequence of the global COVID-19 pandemic. The pandemic continues to have an impact on research progress and career development, especially for graduate students and early career researchers, as strict social distance limitations stifle ongoing research and impede in-person educational programs. The goal of the Bioinformatics Virtual Coordination Network (BVCN) was to reduce some of these impacts by helping research biologists learn new skills and initiate computational projects as alternative ways to carry out their research. The BVCN was founded in April …
A Novel Approach To Teaching Hidden Markov Models To A Diverse Undergraduate Population, Philip Heller, Pratyusha Pogaru
A Novel Approach To Teaching Hidden Markov Models To A Diverse Undergraduate Population, Philip Heller, Pratyusha Pogaru
Faculty Research, Scholarly, and Creative Activity
Hidden Markov Models (HMMs) are an essential tool for Bioinformatic analysis, with extensive success at finding patterns (e.g. CRISPR arrays or genes of interest) in DNA or protein sequences. HMMs are conceptually intricate, and the algorithms that make use of them are complicated. Thus they present a challenge to Bioinformatics instructors at the undergraduate level, particularly when the students’ educational backgrounds are broadly diverse. At San Jose State University, many undergraduate Bioinformatics students are Biology majors with little or no prior coursework in mathematics, statistics, or programming. For this population a theory-based approach to teaching HMMs would be ineffective. To …