Comparison of BiClusO with Five Different Biclustering Algorithms Using Biological and Synthetic Data

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

doi:10.1007/978-3-030-05414-4_46

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…The biclusters in an MTI are called miRNA-regulatory modules (MRM). We recently developed a biclustering algorithm called BiClusO [12,13]. This algorithm was mainly developed for identifying biclusters from a bipartite graph as the miRNA-mRNA network we used in this study.…”

Section: Mrm Extractionmentioning

confidence: 99%

“…Graph convolutional and deep learning methods are also popular technique on prioritizing or predicting the outcome of a gene or disease from such network [9][10][11]. In the current work, we mainly focused on MRM detection from MTIs by a new biclustering approach we recently developed [12,13]. We then searched the IBD related genes in MRMs detected in MTI networks.…”

Section: Introductionmentioning

confidence: 99%

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Discovery of inflammatory bowel disease-associated miRNAs using a novel bipartite clustering approach

Altaf‐Ul‐Amin

Karim

et al. 2020

BMC Med Genomics

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Background: Multidimensional data mining from an integrated environment of different data sources is frequently performed in computational system biology. The molecular mechanism from the analysis of a complex network of gene-miRNA can aid to diagnosis and treatment of associated diseases. Methods: In this work, we mainly focus on finding inflammatory bowel disease (IBD) associated microRNAs (miRNAs) by biclustering the miRNA-target interactions aided by known IBD risk genes and their associated miRNAs collected from several sources. We rank different miRNAs by attributing to the dataset size and connectivity of IBD associated genes in the miRNA regulatory modules from biclusters. We search the association of some top-ranking miRNAs to IBD related diseases. We also search the network of discovered miRNAs to different diseases and evaluate the similarity of those diseases to IBD. Results: According to different literature, our results show the significance of top-ranking miRNA to IBD or related diseases. The ratio analysis supports our ranking method where the top 20 miRNA has approximately tenfold attachment to IBD genes. From disease-associated miRNA network analysis we found that 71% of different diseases attached to those miRNAs show more than 0.75 similarity scores to IBD. Conclusion: We successfully identify some miRNAs related to IBD where the scoring formula and disease-associated network analysis show the significance of our method. This method can be a promising approach for isolating miRNAs for similar types of diseases.

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Section: Mrm Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discovery of inflammatory bowel disease-associated miRNAs using a novel bipartite clustering approach

Altaf‐Ul‐Amin

Karim

et al. 2020

BMC Med Genomics

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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%

“…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%

“…A total of 55 out of the 563 initial statistically significant clusters were finally proposed as candidate disease-associated modules after adjusting the p-value for multiple testing (Table S3). These 55 clusters comprised a total of 195 different genes (17 HDEGs,59 ODEGs and 119 OGs). Therefore, after excluding the 17 HDEGs, these 178 other genes (with adjusted p-value < 0.05) were proposed as new HSCR candidate genes (Table S4).…”

Section: Identification and Enrichment Analysis Of Candidate Disease-...mentioning

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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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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Comparison of BiClusO with Five Different Biclustering Algorithms Using Biological and Synthetic Data

Cited by 3 publications

References 10 publications

Discovery of inflammatory bowel disease-associated miRNAs using a novel bipartite clustering approach

Discovery of inflammatory bowel disease-associated miRNAs using a novel bipartite clustering approach

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

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