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A systematic review of graph-based explorations of PPI networks: methods, resources, and best practices

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Abstract

This systematic review aims to provide a comprehensive overview of graph-based methodologies utilized in the analysis of protein–protein interaction (PPI) networks. The primary objective is to synthesize existing literature and identify key methodologies, resources, and best practices in the field, with a focus on their application in uncovering essential cancer proteins. A systematic literature search was conducted across various databases to identify relevant studies focusing on graph-based explorations of PPI networks. The selected articles were critically reviewed, and data were extracted regarding the methodologies employed, resources utilized, and best practices identified. The review proceeds to outline a workflow that illustrates the systematic process from the compilation of gene/protein datasets to the generation of essential cancer proteins. A case study on “uncovering essential cancer proteins in breast cancer” was included to exemplify the application of graph-based methodologies in a real-world scenario. The review revealed various graph-based methodologies utilized in PPI network analysis, including centrality measures, pathway enrichment analyses, and network visualization techniques. Essential resources such as databases, software tools, and repositories were identified, along with best practices for data preprocessing, network construction, and analysis. The synthesis of findings, complemented by the case study, provides researchers with a comprehensive understanding of the current landscape of graph-based PPI network analysis and its application in cancer research. This systematic review contributes to the field by offering a holistic overview of graph-based explorations in PPI network research, with a specific focus on cancer protein identification. By synthesizing existing knowledge and identifying essential resources and best practices, this review serves as a valuable resource for researchers, facilitating informed decision-making and enhancing research quality and reproducibility. The inclusion of the case study underscores the practical application of graph-based methodologies in uncovering essential cancer proteins.

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References

  • (2023) Uniprot: the universal protein knowledgebase in 2023. Nucl Acids Res 51(D1):D523–D531

  • Ahmed MR, Rehana H, Asaduzzaman S (2021) Protein interaction network and drug design of stomach cancer and associated disease: a bioinformatics approach. J Proteins Proteom 12:33–43

    Article  Google Scholar 

  • Alcalá A, Alberich R, Llabrés M et al (2020) Alignet: alignment of protein–protein interaction networks. BMC Bioinform 21:1–22

    Article  Google Scholar 

  • Amala A, Emerson IA (2019) Identification of target genes in cancer diseases using protein–protein interaction networks. Netw Model Anal Health Inform Bioinform 8:1–13

    Article  Google Scholar 

  • Ashtiani M, Salehzadeh-Yazdi A, Razaghi-Moghadam Z et al (2018) A systematic survey of centrality measures for protein–protein interaction networks. BMC Syst Biol 12(1):1–17

    Article  Google Scholar 

  • Assenov Y, Ramírez F, Schelhorn SE et al (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284

    Article  Google Scholar 

  • Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:1–11

    Article  Google Scholar 

  • Bajpai AK, Davuluri S, Tiwary K et al (2020) Systematic comparison of the protein–protein interaction databases from a user’s perspective. J Biomed Inform 103:103380

    Article  Google Scholar 

  • Bakhtiarnia A, Fahim A, Miandoab EM (2021) Parameter identification of complex network dynamics. Nonlinear Dyn 104(4):3991–4005

    Article  Google Scholar 

  • Baudot A, Gomez-Lopez G, Valencia A (2009) Translational disease interpretation with molecular networks. Genome Biol 10:1–9

    Article  Google Scholar 

  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc Ser B (Methodol) 57(1):289–300

    Article  MathSciNet  Google Scholar 

  • Bloch F, Jackson MO, Tebaldi P (2023) Centrality measures in networks. Soc Choice Welf 61(2):413–453

    Article  MathSciNet  Google Scholar 

  • Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5):1170–1182

    Article  Google Scholar 

  • Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Soc Netw 30(2):136–145

    Article  Google Scholar 

  • Brohee S, Faust K, Lima-Mendez G et al (2008) Neat: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucl Acids Res 36(suppl-2):W444–W451

    Article  Google Scholar 

  • Calderone A, Iannuccelli M, Peluso D et al (2020) Using the mint database to search protein interactions. Curr Protocols Bioinform 69(1):e93

    Article  Google Scholar 

  • Cao B, Luo L, Feng L et al (2017) A network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer. BMC Cancer 17:1–13

    Article  Google Scholar 

  • Chabot C, Stolte C, Hanrahan P (2003) Tableau software. Tableau Softw 6:1

    Google Scholar 

  • Chen C, Shen H, Zhang LG et al (2016) Construction and analysis of protein–protein interaction networks based on proteomics data of prostate cancer. Int J Mol Med 37(6):1576–1586

    Article  Google Scholar 

  • Chen SJ, Liao DL, Chen CH et al (2019) Construction and analysis of protein–protein interaction network of heroin use disorder. Sci Rep 9(1):1–9

    Google Scholar 

  • Cherven K (2015) Mastering Gephi network visualization. Packt Publishing Ltd, London

    Google Scholar 

  • Chin CH, Chen SH, Wu HH et al (2014) cytohubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8:1–7

    Article  Google Scholar 

  • Clermont G, Auffray C, Moreau Y et al (2009) Bridging the gap between systems biology and medicine. Genome Med 1:1–6

    Article  Google Scholar 

  • Clough E, Barrett T (2016) The gene expression omnibus database. Stat Genom Methods Protocols 2016:93–110

    Article  Google Scholar 

  • Coordinators NR (2016) Database resources of the national center for biotechnology information. Nucl Acids Res 44(D1):D7–D19

    Article  Google Scholar 

  • Csardi G, Nepusz T et al (2006) The igraph software package for complex network research. InterJ Complex Syst 1695(5):1–9

    Google Scholar 

  • Dalkılıç F, Işik Z (2021) Compound target identification in tissue-specific interaction networks. IEEE Access 9:81702–81716

    Article  Google Scholar 

  • Del Toro N, Shrivastava A, Ragueneau E et al (2022) The intact database: efficient access to fine-grained molecular interaction data. Nucl Acids Res 50(D1):D648–D653

    Article  Google Scholar 

  • Finn RD, Miller BL, Clements J et al (2014) iPfam: a database of protein family and domain interactions found in the protein data bank. Nucl Acids Res 42(D1):D364–D373

    Article  Google Scholar 

  • Freeman LC et al (2002) Centrality in social networks: conceptual clarification. Soc Netw Crit Concepts Sociol Lond Routledge 1:238–263

    Google Scholar 

  • Ge BK, Hu GM, Chen R et al (2022) Msclustering: a cytoscape tool for multi-level clustering of biological networks. Int J Mol Sci 23(22):14240

    Article  Google Scholar 

  • Ghandi M, Huang FW, Jané-Valbuena J et al (2019) Next-generation characterization of the cancer cell line encyclopedia. Nature 569(7757):503–508

    Article  Google Scholar 

  • Goel N, Khandnor P et al (2020) TCGA: a multi-genomics material repository for cancer research. Mater Today Proc 28:1492–1495

    Article  Google Scholar 

  • Good P (2013) Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer, London

    Google Scholar 

  • Hagberg A, Conway D (2020) Networkx: network analysis with Python. https://networkx.github.io

  • Hasan MR, Paul BK, Ahmed K et al (2020) Design protein–protein interaction network and protein–drug interaction network for common cancer diseases: a bioinformatics approach. Inform Med Unlock 18:100311

    Article  Google Scholar 

  • Huang DW, Sherman BT, Tan Q et al (2007) The David gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8(9):1–16

    Article  Google Scholar 

  • Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucl Acids Res 37(1):1–13

    Article  Google Scholar 

  • Iragne F, Nikolski M, Mathieu B et al (2005) ProViz: protein interaction visualization and exploration. Bioinformatics 21(2):272–274

    Article  Google Scholar 

  • Jardim VC, Santos SdS, Fujita A et al (2019) BioNetStat: a tool for biological networks differential analysis. Front Genet 10:594

    Article  Google Scholar 

  • Jeong H, Mason SP, Barabási AL et al (2001) Lethality and centrality in protein networks. Nature 411(6833):41–42

    Article  Google Scholar 

  • Jiang M, Chen Y, Zhang Y et al (2013) Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein–protein interaction network. Mol BioSyst 9(11):2720–2728

    Article  Google Scholar 

  • Jonsson PF, Bates PA (2006) Global topological features of cancer proteins in the human interactome. Bioinformatics 22(18):2291–2297

    Article  Google Scholar 

  • Junker BH, Koschützki D, Schreiber F (2006) Exploration of biological network centralities with CentiBiN. BMC Bioinform 7(1):1–7

    Article  Google Scholar 

  • Kar G, Gursoy A, Keskin O (2009) Human cancer protein–protein interaction network: a structural perspective. PLoS Comput Biol 5(12):e1000601

    Article  Google Scholar 

  • Keshava Prasad T, Goel R, Kandasamy K et al (2009) Human protein reference database-2009 update. Nucl Acids Res 37(suppl–1):D767–D772

    Article  Google Scholar 

  • Klein B, Holmér L, Smith KM et al (2021) A computational exploration of resilience and evolvability of protein–protein interaction networks. Commun Biol 4(1):1352

    Article  Google Scholar 

  • Kulkarni P, Wiley HS, Levine H et al (2023) Addressing the genetic/nongenetic duality in cancer with systems biology. Trends Cancer 2023:1

    Google Scholar 

  • Li M, Li D, Tang Y et al (2017) Cytocluster: a cytoscape plugin for cluster analysis and visualization of biological networks. Int J Mol Sci 18(9):1880

    Article  Google Scholar 

  • Liang B, Li C, Zhao J (2016) Identification of key pathways and genes in colorectal cancer using bioinformatics analysis. Med Oncol 33:1–8

    Article  Google Scholar 

  • Lin C, Cho Y, Hwang WC et al (2007) Clustering methods in protein–protein interaction network. In: Knowledge discovery in bioinformatics: techniques, methods and application, pp 1–35

  • Lin CY, Chin CH, Wu HH et al (2008) Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology. Nucl Acids Res 36(suppl-2):W438–W443

    Article  Google Scholar 

  • Liu X, Hong Z, Liu J et al (2020) Computational methods for identifying the critical nodes in biological networks. Briefings Bioinform 21(2):486–497

    Article  Google Scholar 

  • Liu X, Li X, Fiumara G et al (2023) Link prediction approach combined graph neural network with capsule network. Expert Syst Appl 212:118737

    Article  Google Scholar 

  • Lombardo G, Poggi A, Tomaiuolo M (2022) Continual representation learning for node classification in power-law graphs. Fut Gener Comput Syst 128:420–428

    Article  Google Scholar 

  • Lü J, Wang P, Lü J et al (2020) Statistical analysis of functional genes in human PPI networks. Model Anal Biomol Netw 2020:397–426

    Article  Google Scholar 

  • Luo T, Wu S, Shen X et al (2013) Network cluster analysis of protein–protein interaction network identified biomarker for early onset colorectal cancer. Mol Biol Rep 40:6561–6568

    Article  Google Scholar 

  • Ma H, He Z, Chen J et al (2021) Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of cytohubba. Sci Rep 11(1):1331

    Article  Google Scholar 

  • Maddah R, Molavi Z, Ehymayed HM et al (2024) Identification of shared hub genes and pathways between gastric cancer and helicobacter pylori infection through bioinformatics analysis. Human Gene 39:201237

    Article  Google Scholar 

  • Masood MMD, Manjula D, Sugumaran V (2018) Identification of new disease genes from protein–protein interaction network. J Ambient Intell Human Comput 2018:1–9

    Google Scholar 

  • Meena C, Hens C, Acharyya S et al (2023) Emergent stability in complex network dynamics. Nat Phys 19(7):1033–1042

    Article  Google Scholar 

  • Mellor JC, Yanai I, Clodfelter KH et al (2002) Predictome: a database of putative functional links between proteins. Nucl Acids Res 30(1):306–309

    Article  Google Scholar 

  • Meng X, Li W, Peng X et al (2021) Protein interaction networks: centrality, modularity, dynamics, and applications. Front Comput Sci 15:1–17

    Article  Google Scholar 

  • Mortezapour M, Tapak L, Bahreini F et al (2023) Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis. Comput Biol Med 157:106779

    Article  Google Scholar 

  • Mottaghi-Dastjerdi N, Ghorbani A, Montazeri H et al (2023) A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis. BMC Gastroenterol 23(1):248

    Article  Google Scholar 

  • Mrvar A, Batagelj V (2016) Analysis and visualization of large networks with program package Pajek. Complex Adapt Syst Model 4:1–8

    Article  Google Scholar 

  • Murphy M, Brown G, Wallin C et al (2021) Gene help: integrated access to genes of genomes in the reference sequence collection. In: Gene Help (Internet). National Center for Biotechnology Information (US)

  • Najma, Farooqui A (2023) Biological networks analysis. In: Biological networks in human health and disease. Springer, London, pp 15–49

  • Newman ME (2005) A measure of betweenness centrality based on random walks. Soc Netw 27(1):39–54

    Article  Google Scholar 

  • Nithya C, Kiran M, Nagarajaram HA (2023a) Dissection of hubs and bottlenecks in a protein–protein interaction network. Comput Biol Chem 102:107802

    Article  Google Scholar 

  • Nithya C, Kiran M, Nagarajaram HA (2023b) Hubs and bottlenecks in protein–protein interaction networks. In: Reverse engineering of regulatory networks, pp 227–248

  • Niu B, Liang C, Lu Y et al (2020) Glioma stages prediction based on machine learning algorithm combined with protein–protein interaction networks. Genomics 112(1):837–847

    Article  Google Scholar 

  • Oughtred R, Rust J, Chang C et al (2021) The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30(1):187–200

    Article  Google Scholar 

  • Page MJ, Moher D, Bossuyt PM et al (2021) Prisma 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 372:1

    Google Scholar 

  • Pattin KA, Moore JH (2009) Role for protein–protein interaction databases in human genetics. Expert Rev Proteom 6(6):647–659

    Article  Google Scholar 

  • Pavlopoulos GA, Hooper SD, Sifrim A et al (2011a) Medusa: a tool for exploring and clustering biological networks. BMC Rese Not 4(1):1–6

    Google Scholar 

  • Pavlopoulos GA, Secrier M, Moschopoulos CN et al (2011b) Using graph theory to analyze biological networks. BioData Min 4:1–27

    Article  Google Scholar 

  • Phipson B, Smyth GK (2010) Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Stat Appl Genet Mol Biol 9(1):1

    Article  MathSciNet  Google Scholar 

  • Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J et al (2020) The disgenet knowledge platform for disease genomics: 2019 update. Nucl Acids Res 48(D1):D845–D855

    Google Scholar 

  • Raman K (2010) Construction and analysis of protein–protein interaction networks. Autom Experiment 2:1–11

    Article  Google Scholar 

  • Ran J, Li H, Fu J et al (2013) Construction and analysis of the protein–protein interaction network related to essential hypertension. BMC Syst Biol 7:1–12

    Article  Google Scholar 

  • Rao VS, Srinivas K, Sujini G et al (2014) Protein–protein interaction detection: methods and analysis. Int J Proteom 2014:1

    Article  Google Scholar 

  • Reimand J, Isserlin R, Voisin V et al (2019) Pathway enrichment analysis and visualization of omics data using G: Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc 14(2):482–517

    Article  Google Scholar 

  • Rivera CG, Vakil R, Bader JS (2010) Nemo: network module identification in Cytoscape. BMC Bioinform 11:1–9

    Article  Google Scholar 

  • Rossetti G, Milli L, Cazabet R (2019) Cdlib: a Python library to extract, compare and evaluate communities from complex networks. Appl Netw Sci 4(1):1–26

    Article  Google Scholar 

  • Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M et al (2014) Protein–protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench 7(1):17

    Google Scholar 

  • Saito R, Smoot ME, Ono K et al (2012) A travel guide to Cytoscape plugins. Nat Methods 9(11):1069–1076

    Article  Google Scholar 

  • Salwinski L, Miller CS, Smith AJ et al (2004) The database of interacting proteins: 2004 update. Nucl Acids Res 32(suppl-1):D449–D451

    Article  Google Scholar 

  • Sanz-Pamplona R, Berenguer A, Sole X et al (2012) Tools for protein–protein interaction network analysis in cancer research. Clin Transl Oncol 14:3–14

    Article  Google Scholar 

  • Scardoni G, Tosadori G, Faizan M et al (2014) Biological network analysis with centiscape: centralities and experimental dataset integration. F1000Research 3:1

    Article  Google Scholar 

  • Secrier M, Pavlopoulos GA, Aerts J et al (2012) Arena3D: visualizing time-driven phenotypic differences in biological systems. BMC Bioinform 13:1–11

    Article  Google Scholar 

  • Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  Google Scholar 

  • Suderman M, Hallett M (2007) Tools for visually exploring biological networks. Bioinformatics 23(20):2651–2659

    Article  Google Scholar 

  • Szalay-Bekő M, Palotai R, Szappanos B et al (2012) Moduland plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality. Bioinformatics 28(16):2202–2204

    Article  Google Scholar 

  • Szklarczyk D, Kirsch R, Koutrouli M et al (2023) The string database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucl Acids Res 51(D1):D638–D646

    Article  Google Scholar 

  • Tadaka S, Kinoshita K (2016) NCMine: core-peripheral based functional module detection using near-clique mining. Bioinformatics 32(22):3454–3460

    Article  Google Scholar 

  • Tang Y, Li M, Wang J et al (2015) CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 127:67–72

    Article  Google Scholar 

  • Tate JG, Bamford S, Jubb HC et al (2019) Cosmic: the catalogue of somatic mutations in cancer. Nucl Acids Res 47(D1):D941–D947

    Article  Google Scholar 

  • Theodosiou T, Efstathiou G, Papanikolaou N et al (2017) Nap: the network analysis profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks. BMC Res Notes 10:1–9

    Article  Google Scholar 

  • Tumuluru P, Ravi B (2017) Dijkstra’s based identification of lung cancer related genes using PPI networks. Int J Comput Appl 975:8887

    Google Scholar 

  • Utriainen M, Morris JH (2023) clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape. BMC Bioinform 24(1):134

    Article  Google Scholar 

  • Vella D, Marini S, Vitali F et al (2018) MTGO: PPI network analysis via topological and functional module identification. Sci Rep 8(1):5499

    Article  Google Scholar 

  • Wahab Khattak F, Salamah Alhwaiti Y, Ali A et al (2021) Protein–protein interaction analysis through network topology (oral cancer). J Healthc Eng 2021:1

    Article  Google Scholar 

  • Wang E, Lenferink A, O’Connor-McCourt M (2007a) Cancer systems biology: exploring cancer-associated genes on cellular networks. Preprint arXiv:0712.3753

  • Wang E, Lenferink A, O’Connor-McCourt M (2007b) Genetic studies of diseases: cancer systems biology: exploring cancer-associated genes on cellular networks. Cell Mol Life Sci 64:1752–1762

    Article  Google Scholar 

  • Wang J, Li M, Wang H et al (2011) Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans Comput Biol Bioinf 9(4):1070–1080

    Article  MathSciNet  Google Scholar 

  • Wang S, Huang G, Hu Q et al (2016) A network-based method for the identification of putative genes related to infertility. Biochim Biophys Acta (BBA) General Subj 1860(11):2716–2724

    Article  Google Scholar 

  • Wang Y, Zhou Z, Chen L et al (2021) Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis. Mol Cell Biochem 476:931–939

    Article  Google Scholar 

  • Wilks C, Cline MS, Weiler E et al (2014) The cancer genomics hub (CGHub): overcoming cancer through the power of torrential data. Database 2014:bau093

    Article  Google Scholar 

  • Winter C, Henschel A, Kim WK et al (2006) SCOPPI: a structural classification of protein–protein interfaces. Nucl Acids Res 34(suppl-1):D310–D314

    Article  Google Scholar 

  • Winterhalter C, Widera P, Krasnogor N (2014) JEPETTO: a Cytoscape plugin for gene set enrichment and topological analysis based on interaction networks. Bioinformatics 30(7):1029–1030

    Article  Google Scholar 

  • Wu B, Xi S (2021) Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer. BMC Cancer 21(1):733

    Article  Google Scholar 

  • Wu Z, Zhao X, Chen L (2009) Identifying responsive functional modules from protein–protein interaction network. Mol Cells 27:271–277

    Article  Google Scholar 

  • Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein–protein interaction network. Bioinformatics 22(22):2800–2805

    Article  Google Scholar 

  • Yan W, Xue W, Chen J et al (2016) Biological networks for cancer candidate biomarkers discovery. Cancer Inform 15:CIN-S39458

    Article  Google Scholar 

  • Yang H, Xue J, Li J et al (2020) Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis. Mol Genet Genomic Med 8(6):e1200

    Article  Google Scholar 

  • Yang Y, Zhu Y, Li X et al (2021) Identification of potential biomarkers and metabolic pathways based on integration of metabolomic and transcriptomic data in the development of breast cancer. Arch Gynecol Obstet 303:1599–1606

    Article  Google Scholar 

  • Yu D, Kim M, Xiao G et al (2013) Review of biological network data and its applications. Genom Inform 11(4):200

    Article  Google Scholar 

  • Yu H, Kim PM, Sprecher E et al (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3(4):e59

    Article  MathSciNet  Google Scholar 

  • Zamanian-Azodi M, Rezaei-Tavirani M, Rahmati-Rad S et al (2015) Protein–protein interaction network could reveal the relationship between the breast and colon cancer. Gastroenterol Hepatol Bed Bench 8(3):215

    Google Scholar 

  • Zeng X, Shi G, He Q et al (2021) Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis. Sci Rep 11(1):20799

    Article  Google Scholar 

  • Zhang P, Itan Y (2019) Biological network approaches and applications in rare disease studies. Genes 10(10):797

    Article  Google Scholar 

  • Zhang P, Wang J, Li X et al (2008) Clustering coefficient and community structure of bipartite networks. Physica A 387(27):6869–6875

    Article  Google Scholar 

  • Zhong J, Tang C, Peng W et al (2021) A novel essential protein identification method based on PPI networks and gene expression data. BMC Bioinform 22(1):248

    Article  Google Scholar 

  • Zhou G, Soufan O, Ewald J et al (2019) Networkanalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucl Acids Res 47(W1):W234–W241

    Article  Google Scholar 

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We acknowledge the DST-FIST Bioinformatics Lab, IIIT Bhubaneswar for the computational facilities used in this work.

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Rout, T., Mohapatra, A. & Kar, M. A systematic review of graph-based explorations of PPI networks: methods, resources, and best practices. Netw Model Anal Health Inform Bioinforma 13, 29 (2024). https://doi.org/10.1007/s13721-024-00467-0

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