DPClusSBO: An integrated software for clustering of simple and bipartite graphs

Karim, Mohammad Bozlul; Kanaya, Shigehiko; Altaf‐Ul‐Amin, Md.

doi:10.1016/j.softx.2021.100821

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…The application of biclustering algorithms can solve the traditional problems associated with the discovery of regulatory modules that control gene transcription in biological model [141]. The biclustering algorithm of BiClusO [54,59,60], as well as the one employed in ComiRNet [142], allowed for the efficient discovery of overlapping and highly cohesive biclusters. Then, an overall relevance score (TSR) [57] based on the exploration of only those biclusters containing genes of interest (known disease-associated genes) was applied to predict Hirschsprung-related miRNAs in this study.…”

Section: Discussionmentioning

confidence: 99%

“…In the first step, we created the HSCR disease-relevant PPI network by selecting data from the Human Integrated Protein-Protein Interaction reference (HIPPIE) database [38] and determined high-density clusters in the PPI network according to DPClusO algorithm by using nine different and independent density values from 0.1 to 0.9 [49][50][51][52][53][54].…”

Section: Identification Of Novel Candidate Disease Genes and Disease-...mentioning

confidence: 99%

“…The interactions between miRNAs and gene targets obtained from each of the three interaction sources were individually represented as a bipartite graph, which is called MTI network. The biclusters (miRNA-regulatory modules, MRM) of these networks were determined by BiClusO algorithm [54,59,60]. Then, we created HSCR-related sub-MRMs from the MRMs obtained by identifying the presence of HSCR genes.…”

Section: Prediction Of Disease-associated Mirnasmentioning

confidence: 99%

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Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Lucena-Padros,

Bravo-Gil,

Tous

et al. 2024

Biomolecules

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Hirschsprung’s disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein–protein interaction (PPI) and miRNA–target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene–disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

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Section: Discussionmentioning

confidence: 99%

Section: Identification Of Novel Candidate Disease Genes and Disease-...mentioning

confidence: 99%

Section: Prediction Of Disease-associated Mirnasmentioning

confidence: 99%

See 1 more Smart Citation

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Lucena-Padros,

Bravo-Gil,

Tous

et al. 2024

Biomolecules

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“…( 4) removes small biclusters from two biclusters. The join bicluster function combines the two biclusters and creates a larger bicluster [14].…”

Section: Similarity Of Marine Compounds and Drugsmentioning

confidence: 99%

“…By entering the input similarity matrix of marine compounds and antibiotic compounds. Then applying the values of the RN, TC, Density, CP and OV parameters to the software [14], the results will be obtained in Table 2 and Fig. 4 below: In Fig.…”

Section: Graph Clustering Of Marine Compounds and Antibacterialmentioning

confidence: 99%

Graph Clustering Based on Chemical Similarity in Marine Compounds and Antibacterial Compounds

Rusdi,

Syahrir,

Siddik

et al. 2023

Advances in Physics Research

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Many potential medicines have been discovered from marine natural products (MNPs). It indicates that marine compounds are essential sources in drug development and discovery. Although many marine compounds show particular biological activity, few are recorded as antibacterial compounds. Therefore, finding the potential compound as an antibacterial compound from a marine organism is still challenging. The aim of this study is to utilize a computational approach to discover potential antibacterial compounds from marine resources. The study focuses on employing the BiClusO algorithm for clustering based on the chemical similarity between marine compounds and antibacterial compounds. The results show that the number of clusters formed for marine biota compounds with antibiotic drug compounds is 4. Then the compounds of marine biota with antibiotic compounds formed 7 clusters. Finally, from these clusters, we obtain compounds that are predicted to have similar properties to antibacterial drugs or compounds. From 73 marine compounds, only Sarasinoside J and (-)-Sarasinoside K are predicted as potent antibacterial compounds.

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Protein-protein interaction network analysis on the whiteleg shrimp Penaeus vannamei and Vibrio parahaemolyticus host-pathogen relationship reveals possible proteins and pathways involved during infection

Rosilan

Waiho

Fazhan

et al. 2023

Aquaculture Reports

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DPClusSBO: An integrated software for clustering of simple and bipartite graphs

Cited by 3 publications

References 10 publications

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Graph Clustering Based on Chemical Similarity in Marine Compounds and Antibacterial Compounds

Protein-protein interaction network analysis on the whiteleg shrimp Penaeus vannamei and Vibrio parahaemolyticus host-pathogen relationship reveals possible proteins and pathways involved during infection

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