Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features

J. Rusakovica; J. Hallinan; A. Wipat; P. Zuliani

doi:10.1515/jib-2014-243

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Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features

J. Rusakovica , J. Hallinan , A. Wipat and P. Zuliani

Published/Copyright: October 18, 2016

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From the journal Journal of Integrative Bioinformatics Volume 11 Issue 2

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MLA
APA
Harvard
Chicago
Vancouver

Rusakovica, J., Hallinan, J., Wipat, A. and Zuliani, P.. "Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features" Journal of Integrative Bioinformatics, vol. 11, no. 2, 2014, pp. 93-105. https://doi.org/10.1515/jib-2014-243

Rusakovica, J., Hallinan, J., Wipat, A. & Zuliani, P. (2014). Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features. Journal of Integrative Bioinformatics, 11(2), 93-105. https://doi.org/10.1515/jib-2014-243

Rusakovica, J., Hallinan, J., Wipat, A. and Zuliani, P. (2014) Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features. Journal of Integrative Bioinformatics, Vol. 11 (Issue 2), pp. 93-105. https://doi.org/10.1515/jib-2014-243

Rusakovica, J., Hallinan, J., Wipat, A. and Zuliani, P.. "Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features" Journal of Integrative Bioinformatics 11, no. 2 (2014): 93-105. https://doi.org/10.1515/jib-2014-243

Rusakovica J, Hallinan J, Wipat A, Zuliani P. Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features. Journal of Integrative Bioinformatics. 2014;11(2): 93-105. https://doi.org/10.1515/jib-2014-243

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Published Online: 2016-10-18

Published in Print: 2014-6-1

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Articles in the same Issue

Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data
A Query Suggestion Workflow for Life Science IR-Systems
The Topology of the Growing Human Interactome Data
VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications
Integrating Sequence Analysis with Biophysical Modelling for Accurate Transcription Start Site Prediction
BioNetLink - An Architecture for Working with Network Data
A distributed computational search strategy for the identification of diagnostics targets: Application to finding aptamer targets for methicillin-resistant staphylococci
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BacillusRegNet: A transcriptional regulation database and analysis platform for Bacillus species