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.
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Acknowledgment
The authors would like to thank Anastasis Tzimas for his insightful remarks and comments.
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Ferles, C., Beaufort, WS., Ferle, V. (2017). Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization. In: Westhead, D., Vijayabaskar, M. (eds) Hidden Markov Models. Methods in Molecular Biology, vol 1552. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6753-7_6
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DOI: https://doi.org/10.1007/978-1-4939-6753-7_6
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