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Multi-view methods for protein structure comparison using latent dirichlet allocation.

Shivashankar S, Srivathsan S, Ravindran B, Tendulkar AV - Bioinformatics (2011)

Bottom Line: It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements.In this article, we propose an improved representation of protein structures using latent dirichlet allocation topic model.We compare the proposed representation and retrieval framework on the benchmark dataset developed by Kolodny and co-workers.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, IIT Madras, Chennai-600 036.

ABSTRACT

Motivation: With rapidly expanding protein structure databases, efficiently retrieving structures similar to a given protein is an important problem. It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements. Recently, researchers proposed vector space model of proteins using bag of fragments representation (FragBag), which corresponds to the basic information retrieval model.

Results: In this article, we propose an improved representation of protein structures using latent dirichlet allocation topic model. Another important requirement is to retrieve proteins, whether they are either close or remote homologs. In order to meet diverse objectives, we propose multi-viewpoint based framework that combines multiple representations and retrieval techniques. We compare the proposed representation and retrieval framework on the benchmark dataset developed by Kolodny and co-workers. The results indicate that the proposed techniques outperform state-of-the-art methods.

Availability: http://www.cse.iitm.ac.in/~ashishvt/research/protein-lda/.

Contact: ashishvt@cse.iitm.ac.in.

Show MeSH
Graphical representation of LDA; K is the number of topics; N is the number of protein structures; Ns is the number of fragments in protein structure s.
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Figure 1: Graphical representation of LDA; K is the number of topics; N is the number of protein structures; Ns is the number of fragments in protein structure s.

Mentions: The graphical model representation of LDA is provided in Figure 1. It models the protein structure collection according to the following generative process:


Multi-view methods for protein structure comparison using latent dirichlet allocation.

Shivashankar S, Srivathsan S, Ravindran B, Tendulkar AV - Bioinformatics (2011)

Graphical representation of LDA; K is the number of topics; N is the number of protein structures; Ns is the number of fragments in protein structure s.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117356&req=5

Figure 1: Graphical representation of LDA; K is the number of topics; N is the number of protein structures; Ns is the number of fragments in protein structure s.
Mentions: The graphical model representation of LDA is provided in Figure 1. It models the protein structure collection according to the following generative process:

Bottom Line: It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements.In this article, we propose an improved representation of protein structures using latent dirichlet allocation topic model.We compare the proposed representation and retrieval framework on the benchmark dataset developed by Kolodny and co-workers.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, IIT Madras, Chennai-600 036.

ABSTRACT

Motivation: With rapidly expanding protein structure databases, efficiently retrieving structures similar to a given protein is an important problem. It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements. Recently, researchers proposed vector space model of proteins using bag of fragments representation (FragBag), which corresponds to the basic information retrieval model.

Results: In this article, we propose an improved representation of protein structures using latent dirichlet allocation topic model. Another important requirement is to retrieve proteins, whether they are either close or remote homologs. In order to meet diverse objectives, we propose multi-viewpoint based framework that combines multiple representations and retrieval techniques. We compare the proposed representation and retrieval framework on the benchmark dataset developed by Kolodny and co-workers. The results indicate that the proposed techniques outperform state-of-the-art methods.

Availability: http://www.cse.iitm.ac.in/~ashishvt/research/protein-lda/.

Contact: ashishvt@cse.iitm.ac.in.

Show MeSH