| Chapter title |
Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization
|
|---|---|
| Chapter number | 6 |
| Book title |
Hidden Markov Models
|
| Published in |
Methods in molecular biology, February 2017
|
| DOI | 10.1007/978-1-4939-6753-7_6 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6751-3, 978-1-4939-6753-7
|
| Authors |
Christos Ferles, William-Scott Beaufort, Vanessa Ferle |
| Editors |
David R. Westhead, M. S. Vijayabaskar |
| Abstract |
The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 50% |
| Researcher | 1 | 25% |
| Student > Bachelor | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 50% |
| Neuroscience | 1 | 25% |
| Nursing and Health Professions | 1 | 25% |