Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

Altaf-Ul-Amin, Md.; Wada, Masayoshi; Kanaya, Shigehiko

doi:10.5402/2012/726429

Cited by 29 publications

(25 citation statements)

References 25 publications

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“…The DPClus algorithm is a graph-clustering algorithm that can be used to extract densely connected nodes as a cluster [25, 32]. This algorithm can be applied to an undirected simple graph G = ( N , E ) that consists of a finite set of nodes N and a finite set of edges E .…”

Section: Methodsmentioning

confidence: 99%

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

Liu

Abdullah

Huang

et al. 2017

BioMed Research International

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Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.

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Section: Methodsmentioning

confidence: 99%

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

Liu

Abdullah

Huang

et al. 2017

BioMed Research International

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“…The numbers of sub graphs of a given density are too many in networks of reasonable size [11]. The algorithm of Georgii et al [15] attempted to find all possible sub graphs of a given density, but the results indicate that the number of clusters as well as computational time would be very high and would not be feasible for big and dense networks.…”

Section: Cluster Filteringmentioning

confidence: 99%

“…We mention here definitions for some terms from our previously published paper [11] which are also relevant to this work.…”

Section: Relevant Definitionsmentioning

confidence: 99%

“…DPClusO [11] has been developed for overlapping clustering while DPClus [13][14] was mainly developed for non overlapping clustering with limited capability of overlapping clustering. The DPClusO algorithm guarantees coverage, that is, each node becomes part of at least one cluster, whereas the DPClus algorithm does not ensure coverage.…”

Section: Major Differences With Dpclusmentioning

confidence: 99%

“…It also uses the social network analysis and organizational network analysis [6]. Different types of network clustering algorithms such as MCL [7], CFinder [8], Affinity clustering [9] along with DPclus [10], DPclusO [11] have emerged. In this work we have developed a GUI tool based on DPClusO algorithm for clustering of simple graphs.…”

Section: Introductionmentioning

confidence: 99%

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[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]DPClusOST: A Software Tool for General Purpose Graph Clustering

Karim

Wakamatsu

Altaf-Ul-Amin

2017

J. Comput. Aided Chem.

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Modern world is incorporating highly connected heterogeneous data due to information sharing through computer and communication technology. These data lead to a complex relation where drilling down and mining are needed for understanding the actual meaning of data. Today any modern computational technique uses graph clustering as a sophisticated technology for data analysis. In this paper we implement a generalized graph clustering algorithm DPClusO with easy operating procedure and clear visualization techniques. DPClusO is enhanced version of DPClus algorithm where overlapping property of clusters is taken into consideration along with density and periphery tracking. User can select different parameters and visualization attributes to render cluster set, single cluster, hierarchical graph etc. and save these data in image and text formats. This paper discusses step by step operation of the proposed software tool using an example network of metabolites collected from KNApSAcK database. This tool successfully generated cohesive groups of structurally similar metabolites. The tool can be used for analysis of network data of any field of studies.

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Clustering of 3D‐Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities

Ohtana

Abdullah

Altaf-Ul-Amin

et al. 2014

Molecular Informatics

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Developing database systems connecting diverse species based on omics is the most important theme in big data biology. To attain this purpose, we have developed KNApSAcK Family Databases, which are utilized in a number of researches in metabolomics. In the present study, we have developed a network-based approach to analyze relationships between 3D structure and biological activity of metabolites consisting of four steps as follows: construction of a network of metabolites based on structural similarity (Step 1), classification of metabolites into structure groups (Step 2), assessment of statistically significant relations between structure groups and biological activities (Step 3), and 2-dimensional clustering of the constructed data matrix based on statistically significant relations between structure groups and biological activities (Step 4). Applying this method to a data set consisting of 2072 secondary metabolites and 140 biological activities reported in KNApSAcK Metabolite Activity DB, we obtained 983 statistically significant structure group-biological activity pairs. As a whole, we systematically analyzed the relationship between 3D-chemical structures of metabolites and biological activities.

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Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

Cited by 29 publications

References 25 publications

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]DPClusOST: A Software Tool for General Purpose Graph Clustering

Clustering of 3D‐Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities

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