Resources
A Hands-on Introduction to Hidden Markov Models
Author(s): Tony Weisstein1, Elena Gracheva2, Zane Goodwin2, Zongtai Qi2, Wilson Leung2, Christopher D. Shaffer2, Sarah C.R. Elgin2
1. Truman State University 2. Washington University in St. Louis
1445 total view(s), 370 download(s)
Description
From the Abstract: In this Lesson, we describe a classroom activity that demonstrates how a Hidden Markov Model (HMM) is applied to predict a eukaryotic gene, focusing on predicting one exon-intron boundary. This HMM lesson is part of the BIOL/CS 370 'Introduction to Bioinformatics' course (Truman State University, MO) and of Bio4342 'Research Explorations in Genomics' (Washington University in St. Louis, MO). The original target student audiences include both Biology and Computer Sciences majors in their junior and senior years, although we believe the model activity would be successful with younger students.
Citation:
Weisstein, A.E., Gracheva, E., Goodwin, Z., Qi, Z., Leung, W., Shaffer, C.D. and Elgin, S.C.R. 2016. A Hands-on Introduction to Hidden Markov Models. CourseSource. https://doi.org/10.24918/cs.2016.8
Cite this work
Researchers should cite this work as follows:
- Weisstein, T. E., Gracheva, E., Goodwin, Z., Qi, Z., Leung, W., Shaffer, C. D., Elgin, S. C. (2019). A Hands-on Introduction to Hidden Markov Models. Network for Integrating Bioinformatics into Life Sciences Education, QUBES Educational Resources. doi:10.25334/Q4ZM88