Dissecting molecular network structures using a network subgraph approach

Huang, Chien-Hung; Zaenudin, Efendi; Tsai, Jeffrey J. P.; Kurubanjerdjit, Nilubon; Dessie, Eskezeia Y.; Ng, Ka-Lok

doi:10.7717/peerj.9556

Cited by 4 publications

(1 citation statement)

References 93 publications

(109 reference statements)

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“…In particular, network analysis can use quantitative approaches to also model interactions between genes, proteins and other biological elements [ 97 , 98 , 99 ]. A general expression to refer to the investigation and modeling of these interactions is “molecular network”, which is becoming very important in cancer research as demonstrated in the applications against different types of neoplasm [ 100 , 101 , 102 ]: pancreas, gastric, lung, ovarian cancers and others are applications of graph theory in this framework.…”

Section: Network-based Approaches Applied To Cancer Research: Graph T...mentioning

confidence: 99%

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

et al. 2022

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The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

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Section: Network-based Approaches Applied To Cancer Research: Graph T...mentioning

confidence: 99%

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

et al. 2022

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Potential biomarkers analysis and protein internal mechanisms by cold plasma treatment: Is proteomics effective to elucidate protein–protein interaction network and biochemical pathway?

Jia

Wang

2023

Food Chemistry

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Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis

Zaenudin,

Wijaya,

Mekala

et al. 2024

CBIO

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Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Results:: We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules. result: We identify and examine the biological meaning of 5-node regulatory modules and pairs of cancer networks which have the smallest HJS values. These two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma, and (ii) breast cancer and pathways in cancer. Some literature studies provide experimental data to support the 5-node regulatory modules. Conclusion: Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.

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Dissecting molecular network structures using a network subgraph approach

Cited by 4 publications

References 93 publications

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

Potential biomarkers analysis and protein internal mechanisms by cold plasma treatment: Is proteomics effective to elucidate protein–protein interaction network and biochemical pathway?

Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis

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