AccScience Publishing / GPD / Volume 1 / Issue 2 / DOI: 10.36922/gpd.v1i2.101
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Network biology: Recent advances and challenges

Pei Wang1*
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1 School of Mathematics and Statistics, Henan University, Kaifeng, 475004, P.R. China
Submitted: 19 May 2022 | Accepted: 15 September 2022 | Published: 6 October 2022
© 2022 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field – network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information mining in biological networks are reviewed. The principles of guilt-by-association and guilt-by-rewiring in network biology and their applications are discussed. Although great advances have been achieved in the field of network biology over the past decades, there are still many challenging issues. First, efficient and reliable network inference algorithms for high-dimensional and highly noisy omics data are still in great demand. Second, the research focus will be on multilayer biological network theory. This plays a critical role in the exploration of the multi-scale or dynamical characteristics of complex biomolecular networks by integrating multi-source heterogeneous omics data. Third, a close cooperation among biologists, medical workers, and researchers from network science is still a prerequisite in the applications of network biology. The rapid development of network biology will undoubtedly raise important clues for understanding complex phenotypes in biological systems.

Biological network
Network construction
Omics data
Informative gene identification
Multilayer network
National Natural Science Foundation of China
Natural Science Foundation of Henan Province
Program for Science and Technology Innovation Talents in Universities of Henan Province
Training Plan of Young Key Teachers in Colleges and Universities of Henan Province

Barabási AL, 2016, Network Science. Cambridge: Cambridge University Press. 


Chen GR, Wang HF, Li X. 2015, Introduction to Complex Networks: Models, Structures and Dynamics. 2nd ed. Beijing: Higher Education Press.


Chen LN, Wang RS, Zhang XS, 2009, Biomolecular Networks: Methods and Applications in Systems Biology. Hoboken: John Wiley and Sons. 


Lü JH, Wang P, 2020, Modeling and Analysis of Bio- Molecular Networks. Singapore, Berlin: Springer. 


Alon U, 2007, An Introduction to Systems Biology: Design Principles of Biological Circuits. London: Chapman and Hall, CRC. 


Costanzo M, VanderSluis B, Koch EN, et al., 2016, A global genetic interaction network maps a wiring diagram of cellular function. Science, 353(6306): aaf1420.


Wang P, Zhang YH, Lü JH, et al., 2015, Functional characteristics of additional positive feedback in genetic circuits. Nonlinear Dyn, 79(1): 397–408.


Rolland T, Taşan M, Charloteaux B, et al., 2014, A proteome-scale map of the human interactome network. Cell, 159(5): 1212–1226. 


Huttlin EL, Ting L, Bruckner RJ, et al., 2015, The BioPlex network: A systematic exploration of the human interactome. Cell, 162(2): 425–440.


Huttlin EL, Bruckner RJ, Paulo JA, et al., 2017, Architecture of the human interactome defines protein communities and disease networks. Nature, 545(7655): 505–509. 


Luck K, Kim DK, Lambourne L, et al., 2020, A reference map of the human binary protein interactome. Nature, 580(7803): 402–408. 


Wan C, Borgeson B, Phanse S, et al., 2015, Panorama of ancient metazoan macromolecular complexes. Nature, 525(7569): 339–344.


Wang P, Chen Y, Lü JH, et al., 2016, Graphical features of functional genes in Human protein interaction network. IEEE Trans Biomed Circuits Syst, 10(3): 707–720.


Lee TI, Rinaldi N, Robert F, et al., 2002, Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298(55943): 799–804.


Bhler J, Marguerat S, 2013, Transcriptional Regulatory Network. Berlin, New York: Springer.


Guelzim N, Bottani S, Bourgine P, et al., 2002, Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet, 31(1): 60–63.


Ravasz E, Somera AL, Mongru DA, et al., 2002, Hierarchical organization of modularity in metabolic networks. Science, 297(5586): 1551–1555.


Pols T, Sikkema HR, Gaastra BF, et al., 2019, A synthetic metabolic network for physicochemical homeostasis. Nat Commun, 10(1): 4239. https://doi.orgt/10.1038/s41467-019-12287-2


Soyer OS, Salathé M, Bonhoeffer S, 2006, Signal transduction networks: Topology, response and biochemical processes. J Theor Biol, 238(2): 416–425. 


Ikeda F, Lahav G, 2013, Signal transduction and signaling networks. Mol Biol Cell, 24(6): 676. https://doi/.org/10.1091/mbc.E12-12-0877


Papin JA, Hunter T, Palsson BO, et al., 2005, Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Bio, 6(2): 99–111.


Langfelder P, Horvath S, 2008, WGCNA: An R package for weighted correlation network analysis. BMC Bioinformat, 9(1): 559.


Zhang B, Horvath S, 2005, A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol, 4(1): 17.


Lai Y, Wu B, Chen L, et al., 2004, A statistical method for identifying differential gene-gene co-expression patterns. Bioinformatics, 20(17): 3146–3155.


Van Dam S, Vosa U, van der Graaf A, et al., 2018, Gene co-expression analysis for functional classification and gene disease predictions. Brief Bioinformat, 19(4): 575–592.


Wang P, Yang CL, Chen H, et al., 2018, Exploring transcription factors reveals crucial members and regulatory networks involved in different abiotic stresses in Brassica napus L. BMC Plant Biol, 18(1): 202. 


Goh K, Cusick ME, Valle D, et al., 2007, The human disease network. Proc Natl Acad Sci U S A, 104(21): 8685–8690.


Barabási Al, Gulbahce N, Loscalzo J, 2011, Network medicine: A network-based approach to human disease. Nat Rev, 12(1): 56–68.


Yıldırım MA, Goh K, Cusick ME, et al., 2007, Drug-target network. Nat Biotech, 25(10): 1119–1126.


Gosak M, Markovic R, Dolensek J, et al., 2018, Network science of biological systems at different scales: A review. Phys Life Rev, 24: 118–135. 


Barabási AL, Oltvai Z, 2004, Network biology: Understanding the cell’s functional organization. Nat Rev Genet, 5(2): 101–113. https://doi.ortg/10.1038/nrg1272


Liu C, Ma YF, Zhao J, et al., 2020, Computational network biology: Data, models, and applications. Phys Rep, 846: 1–66.


Yuan M, Lin Y, 2007, Model selection and estimation in the Gaussian graphical model. Biometrika, 94(1): 19–35. 


Friedman J, Hastie T, Tibshirani R, 2007, Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3): 432–441.


Cai T, Liu W, Luo X, 2011, A constrained ℓ1 minimization approach to sparse precision matrix estimation. J Amer Stat Assoc, 106(494): 594–607.


Young PM, Trevor H, 2007, Penalized logistic regression for detecting gene interactions. Biostatistics, 9(1): 30–50. 


Roy GG, Geard N, Verspoor K, et al., 2020, PoLoBag: Polynomial lasso bagging for signed gene regulatory network inference from expression data. Bioinformatics, 36(21): 5187–5193.


Duarte NC, Becker SA, Jamshidi N, et al., 2007, Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A, 104(6): 1777–1782. 


Wang Y, Joshi T, Xu D, et al., 2006, Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics, 22(19): 2413–2420.


Finkle JD, Wu JJ, Bagheri N, 2018, Windowed granger causal inference strategy improves discovery of gene regulatory networks. Proc Natl Acad Sci U S A, 115(9): 2252–2257.


Wang YX, Huang H, 2014, Review on statistical methods for gene network reconstruction using expression data. J Theor Biol, 362: 53–61.


Xu S, Zhang C, Wang P, et al., 2020, Variational bayesian weighted complex network reconstruction. Inform Sci, 521: 291–306.


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


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


Wu X, Jiang R, Zhang MQ, et al., 2008, Network-based global inference of human disease genes. Mol Syst Biol, 4(1): 189.


Fessenden M, 2017, Protein maps chart the causes of disease. Nature, 549(7671): 293–295.


Wang P, LüJH, Yu XH, 2014, Identification of important nodes in directed biological networks: A network motif approach. PLoS One, 9(8): e106132. 


Bracken CP, Scott HS, Goodall GJ, 2016, A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet, 17(12): 719–732.


Chen LN, Liu R, Liu ZP, et al., 2012, Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep, 2: 342.


Azhagesan K, Ravindran B, Raman K, 2018, Network-based features enable prediction of essential genes across diverse organisms. PLoS One, 13(12): e0208722.


Carson MB, Lu H, 2015, Network-based prediction and knowledge mining of disease genes. BMC Med Genomics, 8(Suppl 2): S9. 


Hou L, Chen M, Cho J, et al., 2014, Guilt-by-rewiring: Gene prioritization through network rewiring in genome wide association studies. Hum Mol Genet, 23(10): 2780–2790.


Li XY, Li WK, Zeng M, et al., 2020, Network-based methods for predicting essential genes or proteins: A survey. Brief Bioinforma, 21(2): 566–583.


Newman ME, 2003, The structure and function of complex networks. SIAM Rev, 45(2): 167–256.


Ulrik B, 2001, A faster algorithm for betweenness centrality. J Math Sociol, 25(2): 163–177.


Shai C, Shlomo H, Scott K, et al., 2007, A model of Internet topology using k-shell decomposition. Proc Natl Acad Sci U S A, 104(27): 11150–11154. 


Chen DB, LüLY, Shang MS, et al., 2012, Identifying influential nodes in complex networks. Physica A Stat Mech Appl, 391(4): 1777–1787. 


Sergey B, Lawrence P, 1998, The anatomy of a large-scale hypertextual web search engine. Comput Networks ISDN Syst, 30: 107–117.


Lü LY, Chen DB, Ren XL, et al., 2016, Vital nodes identification in complex networks. Phys Rep, 650: 1–63.


Xu S, Wang P, 2017, Identifying important nodes by adaptive LeaderRank. Physica A, 469: 654–664.


Xu S, Wang P, Zhang CX, et al., 2019, Spectral learning algorithm reveals propagation capability of complex network. IEEE Trans Cybern, 49(12): 4253–4261.


Watts DJ, Strogatz SH, 1998, Collective dynamics of ‘small-world’ networks. Nature, 393(6684): 440–442. 


Humphries MD, Gurney K, 2008, Network ‘small-worldness: A quantitative method for determining canonical network equivalence. PLoS One, 3(4): e0002051.


Barabási Al, Albert R, 1999, Emergence of scaling in random networks. Science, 286(5439): 509–512.


Yu HY, Braun P, Yildirim MA, et al., 2008, High-quality binary protein interaction map of the yeast interactome network. Science, 322(5898): 104–110.


Wang P, Lü JH, Yu XH, et al., 2015, Duplication and divergence effect on network motifs in undirected bio-molecular networks. IEEE Trans Biomed Circuits Syst, 9(3): 312–320.


Stark C, Breitkreutz BJ, Reguly T, et al., 2006, BioGRID: A general repository for interaction datasets. Nucl Acids Res, 34(90001): D535–D539.


Hamosh A, Scott AF, Amberger JS, et al., 2005, Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucl Acids Res, 33(1): D514–D517.


Fields S, Song O, 1989, A novel genetic system to detect protein-protein interactions. Nature, 340(6230): 245–246.


SenGupta DJ, Zhang B, Kraemer B, et al., 1996, A three-hybrid system to detect RNA-protein interactions in vivo. Proc Natl Acad Sci U S A, 93(16): 8496–8501.


Yang JS, Garriga-Canut M, Link N, et al., 2018, rec- YnH enables simultaneous many-by-many detection of direct protein-protein and protein-RNA interactions. Nat Commun, 9(1): 3747.


Szklarczyk D, Gable AL, Nastou KC, et al., 2021, The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/ measurement sets. Nucl Acids Res, 49(D10: D605–D612. 


Peri S, Navarro JD, Amanchy R, et al., 2003, Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res, 13(10): 2363–2371.


Vázquez A, Flammini A, Maritan A, et al., 2002, Modeling of protein interaction networks. Complexus, 1(1): 38–44.


Rutjes T, 2007, Duplication-Divergence and Proteome Evolution Networks. Netherlands: Technische Universiteit Eindhoven.


Ispolatov I, Krapivsky PL, Yuryev A, 2005, Duplication-divergence model of protein interaction network,” Phys Rev E, Stat Nonlin Soft Matter Phys, 71(6): 061911.


Pastor-Satorras R, Smith E, Solé RV, 2003, Evolving protein interaction networks through gene duplication. J Theor Biol, 222(2): 199–210. 


Xu CS, Liu ZR, Wang R, 2010, How divergence mechanisms influence disassortative mixing property in biology. Physica A Stat Mech Appl, 389(3): 643–650.


Wan X, Cai SM, Zhou J, et al., 2010, Emergence of modularity and disassortativity in protein-protein interaction networks. Chaos, 20(4): 045113. 


Teichmann SA, Babu MM, 2004, Gene regulatory network growth by duplication. Nat Genet, 36(5): 492–496.


Bhan A, Galas DJ, Dewey TG, 2002, A duplication growth model of gene expression networks. Bioinformatics, 18(11): 1486–1493. 


Wang P, Yu XH, LüJH, 2014, Identification and evolution of structurally dominant nodes in protein-protein interaction networks. IEEE Trans Biomed Circuits. Syst, 8(1): 87–97.


Solé RV, Pastor-Satorras R, Smith E, et al., 2002, A model of large-scale proteome evolution. Adv Complex Syst, 5: 43–54. 


Mei GF, Wu XQ, Wang YF, et al., 2018, Compressive-sensing-based structure identification for multilayer networks. IEEE Trans Cybern, 48(2): 754–764.


Wang YF, Wu XQ, Lü JH, et al., 2020, Topology identification in two-layer complex dynamical networks. IEEE Trans. Netw Sci Eng, 7(1): 538–548. 


Wu XQ, Zhao XY, Lü JH, et al., 2016, Identifying topologies of complex dynamical networks with stochastic perturbations. IEEE Trans Control Netw, 3(4): 379–389.


Zhou J, Yu WW, Li XM, et al., 2009, Identifying the topology of a coupled FitzHugh-Nagumo neurobiological network via a pinning mechanism. IEEE Trans Neural Netw, 20(10): 1679–1684.


Liu Q, Ma C, Xiang B, et al., 2021, Inferring network structure and estimating dynamical process from Binary- State data via logistic regression. IEEE Trans Syst Man Cybern, 51(8): 4639–4649. 


Wu RL, Jiang LB, 2021, Recovering dynamic networks in big static datasets. Phys Rep, 912: 1–57.


Remondinin D, Nerettic N, Franceschi C, et al., 2007, Networks from gene expression time series: Characterization of correlation patterns. Int J Bifur Chaos, 17(7): 2477–2483.


Song L, Langfelder P, Horvath S, 2012, Comparison of co-expression measures: Mutual information, correlation, and model based indices. BMC Bioinformat, 13: 328.


Hong S, Chen X, Li J, et al., 2013, Canonical correlation analysis for RNA-seq co-expression networks. Nucl Acids Res, 41(8): e95.


Skinnider M, Squair J, Foster L, 2019, Evaluating measures of association for single-cell transcriptomics. Nat Methods, 16: 381–386.


Liu ZP, 2018, Towards precise reconstruction of gene regulatory networks by data integration. Quant Biol, 6: 113–128.


Zhang X, Zhao X, He K, et al., 2012, Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 28(1): 98–104.


Meyer P, Lafitte F, Bontempi G, 2008, minet: A R/ Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics, 9: 461.


Wu XQ, Wang WH, Zheng WX, 2012, Inferring topologies of complex networks with hidden variables. Phys Rev E, 86(4): 046106. 


Beyer A, Bandyopadhyay S, Ideker T, 2007, Integrating physical and genetic maps: From genomes to interaction networks. Nat Rev Genet, 8(9): 699–710.


Ghanbari M, Lasserre J, Vingron M, 2015, Reconstruction of gene networks using prior knowledge. BMC Syst Biol, 9(1): 84.


Altarawy D, Eid FE, Heath LS, 2017, PEAK: Integrating curated and noisy prior knowledge in gene regulatory network inference. J Comput Biol, 24(9): 863–873. 


Jansen R, Yu H, Greenbaum D, et al., 2003, A bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 302(5644): 449–453.


Xiao N, Zhou A, Kempher M, et al., 2022, Disentangling direct from indirect relationships in association networks. Proc Natl Acad Sci U S A, 119(2): e2109995119.


Chowdhury H, Bhattacharyya D, Kalita J, et al., 2019, (Differential) co-expression analysis of gene expression: A survey of best practices,” IEEE/ACM Trans Comput Biol Bioinform, 17(4): 1154–1173.


Tesson B, Breitling R, Jansen R, 2010, DiffCoEx: A simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics, 11: 497.


Watson M, 2006, CoXpress: Differential co-expression in gene expression data. BMC Bioinformatics, 7: 509. 


Ha J, Baladandayuthapani V, Do K, 2015, DINGO: Differential network analysis in genomics. Bioinformatics, 31(21): 3413–3420.


Wang P, Wang DJ, 2021, Gene differential co-expression networks based on RNA-seq data, construction and its applications. IEEE/ACM Trans Comput Biol Bioinform.


Liu XP, Liu ZP, Zhao XM, et al., 2012, Identifying disease genes and module biomarkers by differential interactions. J Amer Med Inform Assoc, 19(2): 241–248.


Liu XP, Chang X, Liu R, et al., 2017, Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLoS Comput Biol, 13(7): e1005633. 


Tu JJ, Le OY, Yuan Z, et al., 2021, Differential network analysis by simultaneously considering changes in gene interactions and gene expression. Bioinformatics, 37(23): 4414–4423.


Hudson NJ, Reverter A, Dalrymple BP, 2009, A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput Biol, 5(5): 1000382.


Tian WD, Zhang LV, Tasan M, et al., 2008, Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol, 9: S1.


Shin H, Sheu B, Joseph M, et al., 2008, Guilt-by-association feature selection: Identifying biomarkers from proteomic profiles. J Biomed Inform, 41(1): 124–136. 


Kitsak M, Gallos LK, Havlin S, et al., 2010, Identification of influential spreaders in complex networks. Nat Phys, 6(11): 888–893.


Wang P, 2021, Statistical identification of important nodes in biological systems. J Syst Sci Complex, 34(4): 1454–1470.


Lü LY, Zhou T, Zhang QM, et al., 2016, The H-index of a network node and its relation to degree and coreness. Nat Commun, 7: 10168.


Koschützki D, Schwöbbermeyer H, Schreiber F, 2007,  Ranking of network elements based on functional substructures. J Theor Biol, 248(3): 471–479.


Rual JF, Venkatesan K, Hao T, et al., 2005, Towards a proteome-scale map of the human protein-protein interaction network. Nature, 437(7062): 1173–1178.


Cui Y, Cai M, Dai Y, et al., 2018, A hybrid network-based method for the detection of disease-related genes. Physica A, 492: 389–394.


Milo R, Shen-Orr S, Itzkovitz S, et al., 2002, Network motifs: Simple building blocks of complex networks. Science, 298(5594): 824–827.


Shen-Orr SS, Milo R, Mangan S, et al., 2002, Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet, 31(1): 64–68.


Louie B, Higdon R, Kolker E, 2009, A statistical model of protein sequence similarity and function similarity reveals overly-specific function predictions. PLoS One, 4(10): e7546.


Watson JD, 2011, Molecular Biology of the Gene. London: Pearson. 


Sharan R, Ulitsky I, Shamir R, 2007, Network-based prediction of protein function. Mol Syst Biol, 3: 88. 


Klie S, Nikoloski Z, Selbig J, 2014, Biological cluster evaluation for gene function prediction. J Comput Biol, 21(6): 428–445. 


Liu YY, Slotine JJ, Barabási AL, 2011, Controllability of complex networks. Nature, 473(7346): 167–173.


Wuchty S, 2014, Controllability in protein interaction networks. Proc Natl Acad Sci U S A, 111(19): 7156–7160.


Liu XM, Pan LQ, 2015, Identifying driver nodes in the human signaling network using structural controllability analysis. IEEE/ACM Trans Comput Biol Bioinform, 12(2): 467–472.


Zhang XF, Le OY, Zhu Y, et al., 2015, Determining minimum set of driver nodes in protein-protein interaction networks. BMC Bioinformatics, 16: 146. 


Vinayagam A, Gibson TE, Lee HJ, et al., 2016, Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc Natl Acad Sci U S A, 113(18): 4976–4981.


Yan G, Vertes PE, Towlson EK, et al., 2017, Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature, 550(7677): 519–523.


Wang P, Wang DJ, Lü JH, 2019, Controllability analysis of a gene network for Arabidopsis thaliana reveals characteristics of functional gene families. IEEE/ACM Trans Comput Biol Bioinform, 16(3): 912–924.


Guo WF, Zhang SW, Zeng T, et al., 2020, Network control principles for identifying personalized driver genes in cancer. Brief Bioinform, 21(5): 1641–1662. 


Zheng W, Wang DJ, Zou XF, 2019, Control of multilayer biological networks and applied to target identification of complex diseases. BMC Bioinformatics, 20: 271.


Subramanian A, Tamayo P, Mootha VK, et al., 2005, Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 102(43): 15545–15550.


Subramanian A, Kuehn H, Gould J, et al., 2007, GSEA-P: A desktop application for gene set enrichment analysis. Bioinformatics, 23(23): 3251–3253.


Huang DW, Sherman BT Lempicki RA, 2009, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Pro, 4(1): 44–57. https;//


Yu GC, Wang LG, Han YY, et al., 2012, ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS, 16(5): 284–287.


Zheng Q, Wang XJ, 2008, GOEAST: A web-based software toolkit for gene ontology enrichment analysis. Nucl Acids Res, 36: 358–363.


Pawson T, Linding R, 2008, Network medicine. FEBS Lett, 582(8): 1266–1270.


Chen Y, Zhu J, Lum PY, et al., 2008, Variations in DNA  elucidate molecular networks that cause disease. Nature, 452(7186): 429–435. 


Valle I, Roweth HG, Malloy MW, et al., 2021, Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nature Food, 2: 143–155.


Erler JT, Linding R, 2012, Network medicine strikes a blow against breast cancer. Cell, 149(4): 731–733.


Lee MJ, Ye AS, Gardino AK, et al., 2012, Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell, 149(4): 780–794. 


Cheng F, Desai RJ, Handy DE, et al., 2018, Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun, 9: 2691. 


Cheng F, István A, Barabási AL, 2019, Network-based prediction of drug combinations. Nat Commun, 10: 1197.


Zhou Y, Hou Y, Shen J, et al., 2020, Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov, 6: 14.


Gysi DM, Valle TD, Zitnik M, et al., 2021, Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc Natl Acad Sci. U S A, 118(19): e2025581118. 


Stumpf MP, Wiuf C, May RM, 2005, Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proc Natl Acad Sci U S A, 102(12): 4221–4224.


Sathyanarayanan A, Gupta R, Thompson EW, et al., 2020, A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping. Brief Bioinform, 21(6): 1920–1936.


Zander M, Lewsey MG, Clark NM, et al., 2020, Publisher correction: Integrated multi-omics framework of the plant response to jasmonic acid. Nat Plants, 6(8): 1065–1065.


Bodein A, Scott-Boyer MP0, Perin O, et al., 2022, Interpretation of network-based integration from multi-omics longitudinal data. Nucl Acids Res, 50(5): e27.


Taylor KJ, Sims AH, Liang L, et al., 2010, Dynamic changes in gene expression in vivo predict prognosis of tamoxifen-treated patients with breast cancer. Breast Cancer Res, 12(3): R39.


Alcalá-Corona SA, Sandoval-Motta S, Espinal-Enríquez J, et al., 2021, Modularity in biological networks. Front Genet, 12: 1708–1708.


Mucha PJ, Richardson T, Macon K, et al., 2010, Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980): 876–878. https://doi.10.1126/science.1184819


Agostino GD, Scala A, 2014, Networks of Networks: The Last Frontier of Complexity. Berlin: Springer.


Bianconi G, 2018, Multilayer Networks: Structure and Function. Oxford: Oxford University Press. 


Pilosof S, Porter MA, Pascual M, et al., 2017, The multilayer nature of ecological networks. Nat Ecol Evol, 1(4): 0101.


Liu XM, Maiorino E, Halu A, et al., 2020, Robustness and lethality in multilayer biological molecular networks. Nat Commun, 11(1): 6043. 


Shang HX, Liu ZP, 2021, Prioritizing Type 2 diabetes genes by weighted PageRank on bilayer heterogeneous networks. IEEE/ACM Trans Comput Biol Bioinform, 18(1): 336–346. 


Shang HX, Liu ZP, 2020, Network-based prioritization of cancer genes by integrative ranks from multi-omics data. Comput Biol Med, 119: 103692.


Li J, Zhao PX, 2016, Mining functional modules in heterogeneous biological networks using multiplex PageRank approach. Front Plant Sci, 7: 903.

Conflict of interest
The author declares no conflict of interest.
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