DGDCT: A Distributed Grid-Density Based Algorithm for Intrinsic Cluster Detection over Massive Spatial Data

Sarmah, Sauravjyoti; Das, Rajib; Bhattacharyya, Dhruba K.

doi:10.1007/978-3-540-77444-0_22

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Next, PAT determines putative structural units that are enriched with high scoring residues. To do this, PAT employs a density grid clustering algorithm [ 25 ]. First, PAT divides the area of the protein into a number of “grids” of 5 residues and calculates an average target score of each grid.…”

Section: Methodsmentioning

confidence: 99%

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies

et al. 2016

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BackgroundThe identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences.ResultsHere, we describe a new tool, PAT (http://www.kimlab.org/software/pat) that can efficiently identify both domains (with optimized boundaries) and non-domain putative structured units. PAT automatically analyzes various structural properties, evaluates the folding stability, and reports possible structural domains in a given protein sequence. For reliability evaluation of PAT, we applied PAT to identify antibody target molecules based on the notion that soluble and well-defined protein secondary and tertiary structures are appropriate target molecules for synthetic antibodies.ConclusionPAT is an efficient and sensitive tool to identify structured units. A performance analysis shows that PAT can characterize structurally well-defined regions in a given sequence and outperforms other efforts to define reliable boundaries of domains. Specially, PAT successfully identifies experimentally confirmed target molecules for antibody generation. PAT also offers the pre-calculated results of 20,210 human proteins to accelerate common queries. PAT can therefore help to investigate large-scale structured domains and improve the success rate for synthetic antibody generation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1001-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies

et al. 2016

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“…Implementation of GTOM requires massive matrix operations, especially when large datasets [20] are used. The use of GPU in such applications has been justified in [21].…”

Section: Related Workmentioning

confidence: 99%

PNME – A gene-gene parallel network module extraction method

Jaiswal

Kumar

Bhattacharyya

2018

Journal of Genetic Engineering and Biotechnology

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In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.

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“…In [9], clusters are found based on the idea of densityisoline, however, each cluster cannot be separated efficiently. DGDCT [10] is a grid-based clustering algorithm which gets the final clusters from dense grid to directly density confidence-reachable grid. But it is necessary to order the grid by density which is cost much time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Density Clustering Algorithm Based on Grid and Boundary

Wang

2010

2010 International Conference on Computational Intelligence and Software Engineering

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In traditional grid clustering algorithms, the cluster results are just consisted of dense grids so that the clustering quality is low, while these algorithms are unable to cluster the multi-density datasets. In this paper, we propose a clustering algorithm based on grid and boundary over multi-density datasets. In order to describe the data distribution, boundary grid is introduced and checked by the difference of density of adjacency girds. The final clusters are defined as the set of internal grids and boundary grids, which internal grids encircled by boundary grids. Experimental results indicate that our algorithm is able to cluster the multi-density datasets and has a higher clustering quality.

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DGDCT: A Distributed Grid-Density Based Algorithm for Intrinsic Cluster Detection over Massive Spatial Data

Cited by 3 publications

References 7 publications

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies

PNME – A gene-gene parallel network module extraction method

Multi-Density Clustering Algorithm Based on Grid and Boundary

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