Graph-Theory Based Simplification Techniques for Efficient Biological Network Analysis

Ko, Euiseong; Kang, Min-Hee; Chang, Hyung Jae; Kim, Dong-Hyun

doi:10.1109/bigdataservice.2017.39

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…Among the 495 genes, 190 genes (coloured in red in Figure 8) were reported in the String database, and 41 interactions were matched. The simplified sub-components that maximise interaction coefficients of the connectedcomponents were analysed (Ko et al, 2017) for further analysis, as shown in Figure 9. Two gene regulations, shown as lines in red, are in accordance with the report of the String database.…”

Section: Experimental Results With Simulation Datamentioning

confidence: 99%

Integration of multi-omics data for integrative gene regulatory network inference

Zarayeneh

et al. 2017

IJDMB

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Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was Correspondence to: Mingon Kang. This paper is a revised and expanded version of a paper entitled 'Integrative gene regulatory network inference using multi-omics data' presented at the

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Section: Experimental Results With Simulation Datamentioning

confidence: 99%

Integration of multi-omics data for integrative gene regulatory network inference

Zarayeneh

et al. 2017

IJDMB

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among the 495 genes, 190 genes (coloured in red in Figure 8) were reported in the String database, and 41 interactions were matched. The simplified sub-components that maximise interaction coefficients of the connected-components were analysed (Ko et al, 2017) for further analysis, as shown in Figure 9. Two gene regulations, shown as lines in red, are in accordance with the report of the String database.…”

Section: Human Brain Data For Psychiatric Diseasesmentioning

confidence: 99%

Integration of multi-omics data for integrative gene regulatory network inference

Zarayeneh

et al. 2017

IJDMB

Self Cite

View full text Add to dashboard Cite

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

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“…In our proposed approach, after obtaining possible docked poses of fragments, a graph theory algorithm is applied to align a complete ligand with the binding poses of its composing fragments. Graph theory-based methods have been adopted in various biological and bioinformatics studies previously, including metabolic pathway analysis [35,36], protein flexibility predictions [37], protein side chain predictions [38], secondary structure motif comparisons [39], calcium binding site predictions [40] and more. The application of graph theory in the current work has been inspired by earlier studies using graph theory to find maximum common substructures between two structures [41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Combining fragment docking with graph theory to improve ligand docking for homology model structures

Sarfaraz

Muneer

2020

J Comput Aided Mol Des

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Computational protein-ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignments, possible binding poses of complete ligand are determined. This docking method is first tested for bound docking on a series of Cytochrome P450 (CYP450) enzyme-substrate complexes, in which experimentally determined receptor structures are used. For all complexes tested, ligand poses of less than 1 Å root mean square deviations (RMSD) from the actual binding positions can be recovered. Then the method is tested for unbound docking with modeled receptor structures for a number of protein-ligand complexes from different families including the very recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease. For all complexes, poses with RMSD less than 3 Å from actual binding positions can be recovered. Our results suggest that for docking with approximately modeled receptor structures, fragment-based methods can be more effective than common complete ligand docking approaches.

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Graph-Theory Based Simplification Techniques for Efficient Biological Network Analysis

Cited by 9 publications

References 17 publications

Integration of multi-omics data for integrative gene regulatory network inference

Integration of multi-omics data for integrative gene regulatory network inference

Integration of multi-omics data for integrative gene regulatory network inference

Combining fragment docking with graph theory to improve ligand docking for homology model structures

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