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Representing DNA for Machine Learning Algorithms: A Primer on One-Hot, Binary, and Integer Encodings
Peer reviewed
Direct linkYash Munnalal Gupta; Satwika Nindya Kirana; Somjit Homchan – Biochemistry and Molecular Biology Education, 2025
This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning intervention focuses on developing practical skills in implementing these essential techniques for…
Descriptors: Science Instruction, Teaching Methods, Genetics, Molecular Biology
Rebeckah K. Fussell; Megan Flynn; Anil Damle; Michael F. J. Fox; N. G. Holmes – Physical Review Physics Education Research, 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…
Descriptors: Physics, Computational Linguistics, Classification, Laboratory Experiments
Ethan C. Campbell; Katy M. Christensen; Mikelle Nuwer; Amrita Ahuja; Owen Boram; Junzhe Liu; Reese Miller; Isabelle Osuna; Stephen C. Riser – Journal of Geoscience Education, 2025
Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here…
Descriptors: Earth Science, Undergraduate Students, Programming Languages, Computer Software
