[Abarbanel, H. (2012). Analysis of Observed Chaotic Data, Springer Science, New York, NY.]Search in Google Scholar
[Abarbanel, H.D., Brown, R., Sidorowich, J.J. and Tsimring, L.S. (1993). The analysis of observed chaotic data in physical systems, Reviews of Modern Physics65(4): 1331.10.1103/RevModPhys.65.1331]Search in Google Scholar
[Albert, R. and Barabási, A.-L. (2002). Statistical mechanics of complex networks, Reviews of Modern Physics74(1): 47.10.1103/RevModPhys.74.47]Search in Google Scholar
[Alm, E. and Arkin, A.P. (2003). Biological networks, Current Opinion in Structural Biology13(2): 193–202.10.1016/S0959-440X(03)00031-9]Search in Google Scholar
[Altuntaş, V. and Gök, M. (2017). The stability and fragility of biological networks: Eukaryotic model organism saccharomyces cerevisiae, International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey, pp. 116–118.]Search in Google Scholar
[Altuntaş, V. and Gök, M. (2020). Protein–protein etkileşimi tespit yöntemleri, veri tabanları ve veri güvenilirliği, Avrupa Bilim ve Teknoloji Dergisi (19): 722–733.10.31590/ejosat.724390]Search in Google Scholar
[Altuntas, V., Gök, M. and Kahveci, T. (2018). Stability analysis of biological networks’ diffusion state, IEEE/ACM Transactions on Computational Biology and Bioinformatics11(4): 1406–1418.]Search in Google Scholar
[Borgatti, S.P. (2005). Centrality and network flow, Social Networks27(1): 55–71.10.1016/j.socnet.2004.11.008]Search in Google Scholar
[Can, T.,Çamoǧlu, O. and Singh, A.K. (2005). Analysis of protein-protein interaction networks using random walks, Proceedings of the 5th International Workshop on Bioinformatics, Chicago, IL, USA, pp. 61–68.]Search in Google Scholar
[Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series, Physica D: Nonlinear Phenomena110(1–2): 43–50.10.1016/S0167-2789(97)00118-8]Search in Google Scholar
[Cao, M., Zhang, H., Park, J., Daniels, N.M., Crovella, M.E., Cowen, L.J. and Hescott, B. (2013). Going the distance for protein function prediction: A new distance metric for protein interaction networks, PloS One8(10): e76339.10.1371/journal.pone.0076339380681024194834]Search in Google Scholar
[Chatr-Aryamontri, A., Breitkreutz, B.-J., Oughtred, R., Boucher, L., Heinicke, S., Chen, D., Stark, C., Breitkreutz, A., Kolas, N., O’Donnell, L., Reguly, T., Nixon, J., Ramage, L., Winter, A., Sellam, A., Chang, C., Hirschman, J., Theesfeld, C., Rust, J., Livstone, M.S., Dolinski, K. and Tyers, M. (2015). The BioGRID interaction database: 2015 Update, Nucleic Acids Research43(D1): D470–D478.10.1093/nar/gku1204438398425428363]Search in Google Scholar
[Cho, H., Berger, B. and Peng, J. (2015). Diffusion component analysis: Unraveling functional topology in biological networks, International Conference on Research in Computational Molecular Biology, Warsaw, Poland, pp. 62–64.]Search in Google Scholar
[Erten, S., Bebek, G. and Koyutürk, M. (2011). VAVIEN: An algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks, Journal of Computational Biology18(11): 1561–1574.10.1089/cmb.2011.0154321610022035267]Search in Google Scholar
[Freeman, L.C. (1977). A set of measures of centrality based on betweenness, Sociometry40(1): 35–41.10.2307/3033543]Search in Google Scholar
[Freeman, L.C., Borgatti, S.P. and White, D.R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow, Social Networks13(2): 141–154.10.1016/0378-8733(91)90017-N]Search in Google Scholar
[Gabr, H. and Kahveci, T. (2015). Signal reachability facilitates characterization of probabilistic signaling networks, BMC Bioinformatics16(17): S6.10.1186/1471-2105-16-S17-S6467488126679404]Search in Google Scholar
[Gabr, H., Rivera-Mulia, J.C., Gilbert, D.M. and Kahveci, T. (2015). Computing interaction probabilities in signaling networks, EURASIP Journal on Bioinformatics and Systems Biology2015(1): 10.10.1186/s13637-015-0031-8464259926587014]Search in Google Scholar
[Gao, J. (2012). Multiscale analysis of biological data by scale-dependent Lyapunov exponent, Frontiers in Physiology2: 110.10.3389/fphys.2011.00110326495122291653]Search in Google Scholar
[Gök, M., Koçal, O.H. and Genç, S. (2016). Prediction of disordered regions in proteins using physicochemical properties of amino acids, International Journal of Peptide Research and Therapeutics22(1): 31–36.10.1007/s10989-015-9481-9]Search in Google Scholar
[Hagberg, A., Swart, P. and Schult, D. (2008). Exploring network structure, dynamics, and function using network, Technical report, Los Alamos National Lab., Los Alamos, NM.]Search in Google Scholar
[Han, Q. and Wang, P. (2007). Estimation of the largest Lyapunov exponent of the HRV signals, Journal of Biomedical Engineering24(4): 732–735.]Search in Google Scholar
[He, H., Lin, D., Zhang, J., Wang, Y.-P. and Deng, H.-W. (2017). Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network, BMC Bioinformatics18(1), Article no. 149.]Search in Google Scholar
[Hegger, R., Kantz, H. and Schreiber, T. (1999). Practical implementation of nonlinear time series methods: The TISEAN package, Chaos: An Interdisciplinary Journal of Nonlinear Science9(2): 413–435.10.1063/1.16642412779839]Search in Google Scholar
[Holme, P., Kim, B.J., Yoon, C.N. and Han, S.K. (2002). Attack vulnerability of complex networks, Physical Review E65(5): 056109.10.1103/PhysRevE.65.05610912059649]Search in Google Scholar
[Jeong, H., Qian, X. and Yoon, B.-J. (2016). Effective comparative analysis of protein–protein interaction networks by measuring the steady-state network flow using a Markov model, BMC Bioinformatics17(13): 395.10.1186/s12859-016-1215-2507394527766938]Search in Google Scholar
[Kennel, M.B., Brown, R. and Abarbanel, H.D. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A45(6): 3403.10.1103/PhysRevA.45.3403]Search in Google Scholar
[Koçal, O.H., Yuruklu, E. and Avcibas, I. (2008). Chaotic-type features for speech steganalysis, IEEE Transactions on Information Forensics and Security3(4): 651–661.10.1109/TIFS.2008.2004289]Search in Google Scholar
[Köhler, S., Bauer, S., Horn, D. and Robinson, P.N. (2008). Walking the interactome for prioritization of candidate disease genes, The American Journal of Human Genetics82(4): 949–958.10.1016/j.ajhg.2008.02.013242725718371930]Search in Google Scholar
[Li, F., Li, P., Xu, W., Peng, Y., Bo, X. and Wang, S. (2010). Perturbationanalyzer: A tool for investigating the effects of concentration perturbation on protein interaction networks, Bioinformatics26(2): 275–277.10.1093/bioinformatics/btp63419914922]Search in Google Scholar
[Li, Y., Wang, H. and Meng, X. (2019). Almost periodic synchronization of fuzzy cellular neural networks with time-varying delays via state-feedback and impulsive control, International Journal of Applied Mathematics and Computer Science29(2): 337–349, DOI: 10.2478/amcs-2019-0025.10.2478/amcs-2019-0025]Search in Google Scholar
[Liu, K., Wang, H. and Xiao, J. (2015). The multivariate largest Lyapunov exponent as an age-related metric of quiet standing balance, Computational and Mathematical Methods in Medicine2015, Article ID 309756.10.1155/2015/309756444393726064182]Search in Google Scholar
[Nazarimehr, F., Jafari, S., Golpayegani, S.M.R.H. and Sprott, J. (2017). Can Lyapunov exponent predict critical transitions in biological systems?, Nonlinear Dynamics88(2): 1493–1500.10.1007/s11071-016-3325-9]Search in Google Scholar
[Newman, M. (2018). Networks, Oxford University Press, Oxford.10.1093/oso/9780198805090.001.0001]Search in Google Scholar
[Perez, C. and Germon, R. (2016). Graph creation and analysis for linking actors: Application to social data, in R. Layton and P. Watters (Eds), Automating Open Source Intelligence, Elsevier, Waltham, pp. 103–129.10.1016/B978-0-12-802916-9.00007-5]Search in Google Scholar
[Ruiz, D. and Finke, J. (2019). Lyapunov-based anomaly detection in preferential attachment networks, International Journal of Applied Mathematics and Computer Science29(2): 363–373, DOI: 10.2478/amcs-2019-0027.10.2478/amcs-2019-0027]Search in Google Scholar
[Sano, M. and Sawada, Y. (1985). Measurement of the Lyapunov spectrum from a chaotic time series, Physical Review Letters55(10): 1082.10.1103/PhysRevLett.55.108210031723]Search in Google Scholar
[Serletis, A., Shahmoradi, A. and Serletis, D. (2007). Effect of noise on estimation of Lyapunov exponents from a time series, Chaos, Solitons & Fractals32(2): 883–887.10.1016/j.chaos.2005.11.048]Search in Google Scholar
[Stelling, J., Sauer, U., Szallasi, Z., Doyle, F.J. and Doyle, J. (2004). Robustness of cellular functions, Cell118(6): 675–685.10.1016/j.cell.2004.09.00815369668]Search in Google Scholar
[Stumpf, M.P. and Wiuf, C. (2010). Incomplete and noisy network data as a percolation process, Journal of the Royal Society Interface7(51): 1411–1419.10.1098/rsif.2010.0044293560020378609]Search in Google Scholar
[Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K.P., Kuhn, M., Bork, P., Jensen, L.J., von Mering, C. (2014). STRING v10: Protein–protein interaction networks, integrated over the tree of life, Nucleic Acids Research43(D1): D447–D452.10.1093/nar/gku1003438387425352553]Search in Google Scholar
[Turinsky, A.L., Razick, S., Turner, B., Donaldson, I.M. and Wodak, S.J. (2010). Literature curation of protein interactions: Measuring agreement across major public databases, Database2010: baq026, DOI:10.1093/database /baq026.]Search in Google Scholar
[Vocaturo, E. and Veltri, P. (2017). On the use of networks in biomedicine, Procedia Computer Science110: 498–503.10.1016/j.procs.2017.06.132]Search in Google Scholar
[Watts, D.J. and Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks, Nature393(6684): 440.10.1038/309189623998]Search in Google Scholar
[Yu, D., Kim, M., Xiao, G. and Hwang, T.H. (2013). Review of biological network data and its applications, Genomics & Informatics11(4): 200–210.10.5808/GI.2013.11.4.200389784724465231]Search in Google Scholar
[Zhang, X., Wang, H. and Yang, Y. (2016). Robustness of indispensable nodes in controlling protein–protein interaction network, arXiv: 1609.02637.]Search in Google Scholar