BiClusO: A Novel Biclustering Approach and Its Application to Species-VOC Relational Data

Karim, Mohammad Bozlul; Huang, Ming; Ono, Naoaki; Kanaya, Shigehiko; Altaf-Ul-Amin, Md.

doi:10.1109/tcbb.2019.2914901

Cited by 11 publications

(14 citation statements)

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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%

“…1a. BiClusO algorithm generates a reasonable number of overlapping biclusters under the optimized parameter settings [7,12]. In the current work for BiClusO we utilized the following parameter setting: cluster density=0.5, cluster property=0.5, relation number=3, Tanimoto coefficient =0.33 and attachment probability =0.5.…”

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: Mrm Extractionmentioning

confidence: 99%

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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“…Smaller density values resulted in greater cluster sizes and fewer clusters, as expected. As for cp value, 0.5 is the default and recommended value and has been used in previous studies ( Eguchi et al, 2018 ; Karim et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…As for cp value, 0.5 is the default and recommended value and has been used in previous studies (Eguchi et al, 2018;Karim et al, 2020).…”

Section: Gene Co-expression Clusters Analysismentioning

confidence: 99%

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Harun

Afiqah‐Aleng

Karim

et al. 2021

PeerJ

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Background Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset. Methods We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters. Results The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

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Implementation of BiClusO and its comparison with other biclustering algorithms

2019

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This paper describes the implementation of biclustering algorithm BiClusO using graphical user interface and different parameters to generate overlapping biclusters from a binary sparse matrix. We compare our algorithm with several other biclustering algorithms in the context of two different types of biological datasets and four synthetic datasets with known embedded biclusters. Biclustering technique is widely used in different fields of studies for analyzing bipartite relationship dataset. Over the past decade, different biclustering algorithms have been proposed by researchers which are mainly used for biological data analysis. The performance of these algorithms differs depending on dataset size, pattern, and property. These issues create difficulties for a researcher to take the right decision for selecting a good biclustering algorithm. Two different scoring methods along with Gene Ontology(GO) term enrichment analysis have been used to measure and compare the performance of our algorithm. Our algorithm shows the best performance over some other well-known biclustering algorithms.

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BiClusO: A Novel Biclustering Approach and Its Application to Species-VOC Relational Data

Cited by 11 publications

References 50 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

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Implementation of BiClusO and its comparison with other biclustering algorithms

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