Cluster visualization and nonlinear projection techniques for biological sequences

Title:

Cluster visualization and nonlinear projection techniques for biological sequences

Creator:

Ferles, C. and Stafylopatis, A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:745da74e-a346-4780-ad03-7cee98227612
uuid:745da74e-a346-4780-ad03-7cee98227612
doi:10.14311/NNW.2016.26.016

Subject:

non-numerical/symbolical sequence data, DNA, RNA, protein, clustering, visualization, mapping, nonlinear projection, Self-Organizing Map (SOM), and Hidden Markov Model (HMM)

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The present study devises two techniques for visualizing biological sequence data clusterings. 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 preserved as faithfully as possible. The resulting unified framework incorporates directly raw symbolical sequence data (without necessitating any preprocessing stage), and moreover, operates on a pure unsupervised basis under complete absence of prior information and domain knowledge.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:3

Harvested from:

CDK

Metadata only:

false