|
Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 16-34.doi: 10.1007/s11390-019-1896-x
Special Issue: Surveys; Artificial Intelligence and Pattern Recognition; Emerging Areas
• Special Section of Advances in Computer Science and Technology—Current Advances in the NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao 2014-2017 (Part 1) • Previous Articles Next Articles
Lin Wu1, Min Li2, Jian-Xin Wang2, and Fang-Xiang Wu1,2,3,*, Senior Member, IEEE
[1] Ito T, Chiba T, Ozawa R et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences, 2001, 98(8):4569-4574. [2] Sprinzak E, Margalit H. Correlated sequence-signatures as markers of protein-protein interaction. Journal of Molecular Biology, 2001, 311(4):681-692. [3] Liu L Z, Wu F X, Zhang W J. Reverse engineering of gene regulatory networks from biological data. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery, 2012, 2(5):365-385. [4] Gu S, Pasqualetti F, Cieslak M et al. Controllability of structural brain networks. Nature Communications, 2015, 6:Article No. 8414. [5] Csermely P, Agoston V, Pongor S. The efficiency of multitarget drugs:The network approach might help drug design. Trends in Pharmacological Sciences, 2005, 26(4):178-182. [6] Dai Y F, Zhao X M. A survey on the computational approaches to identify drug targets in the postgenomic era. BioMed Research International, 2015, 2015:Article No. 239654. [7] Wang X, Gulbahce N, Yu H. Network-based methods for human disease gene prediction. Briefings in Functional Genomics, 2011, 10(5):280-293. [8] Chen B, Fan W, Liu J et al. Identifying protein complexes and functional modules-From static PPI networks to dynamic PPI networks. Briefings in Bioinformatics, 2013, 15(2):177-194. [9] Kalman R E. Mathematical description of linear dynamical systems. Journal of the Society for Industrial and Applied Mathematics Control, Series A, 1963, 1(2):152-192. [10] Lin C T. Structural controllability. IEEE Transactions on Automatic Control, 1974, 19(3):201-208. [11] Liu Y Y, Slotine J J, Barabási A L. Controllability of complex networks. Nature, 2011, 473(7346):167-173. [12] Wang B, Gao L, Zhang Q et al. Diversified control paths:A significant way disease genes perturb the human regulatory network. PLoS One, 2015, 10(8):Article No. e0135491. [13] Wu L, Shen Y, Li M et al. Network output controllabilitybased method for drug target identification. IEEE Transactions on Nano Bioscience, 2015, 14(2):184-191. [14] Yan G, Vértes P E, Towlson E K et al. Network control principles predict neuron function in the caenorhabditis elegans connectome. Nature, 2017, 550(7677):519-523. [15] D'haeseleer P, Wen X, Fuhrman S et al. Linear modeling of mRNA expression levels during CNS development and injury. Pacific Symposium on Biocomputing, 1999, 4:41-52. [16] Slotine J J, Li W. Applied Nonlinear Control. Pearson, 1991. [17] Liu Y Y, Barabási A L. Control principles of complex systems. Reviews of Modern Physics, 2016, 88(3):Article 035006. [18] Shields R, Pearson J. Structural controllability of multiinput linear systems. IEEE Transactions on Automatic Control, 1976, 21(2):203-212. [19] Glover K, Silverman L. Characterization of structural controllability. IEEE Transactions on Automatic Control, 1976, 21(4):534-537. [20] Hosoe S, Matsumoto K. On the irreducibility condition in the structural controllability theorem. IEEE Transactions on Automatic Control, 1979, 24(6):963-966. [21] Linnemann A. A further simplification in the proof of the structural controllability theorem. IEEE Transactions on Automatic Control, 1986, 31(7):638-639. [22] Hosoe S. Determination of generic dimensions of controllable subspaces and its application. IEEE Transactions on Automatic Control, 1980, 25(6):1192-1196. [23] Poljak S. On the generic dimension of controllable subspaces. IEEE Transactions on Automatic Control, 1990, 35(3):367-369. [24] Murota K, Poljak S. Note on a graph-theoretic criterion for structural output controllability. IEEE Transactions on Automatic Control, 1990, 35(8):939-942. [25] Wu F X, Wu L, Wang J et al. Transittability of complex networks and its applications to regulatory biomolecular networks. Scientific Reports, 2014, 4:Article No. 4819. [26] Mayeda H, Yamada T. Strong structural controllability. SIAM Journal on Control and Optimization, 1979, 17(1):123-138. [27] Tu C. Strong structural control centrality of a complex network. Physica Scripta, 2015, 90(3):Article No. 035202. [28] Nepusz T, Vicsek T. Controlling edge dynamics in complex networks. Nature Physics, 2012, 8(7):568-573. [29] Cowan N J, Chastain E J, Vilhena D A et al. Nodal dynamics, not degree distributions, determine the structural controllability of complex networks. PLoS One, 2012, 7(6):Article No. e38398. [30] Nie S, Wang X, Zhang H et al. Robustness of controllability for networks based on edge-attack. PLoS One, 2014, 9(2):Article No. e89066. [31] Wang W X, Ni X, Lai Y C et al. Optimizing controllability of complex networks by minimum structural perturbations. Physical Review E, 2012, 85(2):Article No. 026115. [32] Wu L, Li M, Wang J et al. CytoCtrlAnalyser:A cytoscape app for biomolecular network controllability analysis. Bioinformatics, 2018, 34(8):1428-1430. [33] Wu L, Li M, Wang J et al. Minimum steering node set of complex networks and its applications to biomolecular networks. IET Systems Biology, 2016, 10(3):116-123. [34] Liu Y Y, Slotine J J, Barabási A L. Control centrality and hierarchical structure in complex networks. PLoS One, 2012, 7(9):Article No. e44459. [35] Iudice F L, Garofalo F, Sorrentino F. Structural permeability of complex networks to control signals. Nature Communications, 2015, 6:Article No. 8349. [36] Liu X, Pan L. Controllability of the better chosen partial networks. Physica A:Statistical Mechanics and Its Applications, 2016, 456:120-127. [37] Commault C, van der Woude J, Boukhobza T. On the fixed controllable subspace in linear structured systems. Systems & Control Letters, 2017, 102:42-47. [38] Wu L, Shen Y, Li M et al. Drug target identification based on structural output controllability of complex networks. In Proc. the 10th International Symposium Bioinformatics Research and Applications, June 2014, pp.188-199. [39] Gao J, Liu Y Y, D'Souza R M et al. Target control of complex networks. Nature Communications, 2014, 5:Article No. 5415. [40] Ogata K. Modern Control Engineering (3rd edition). Prentice Hall, 1996. [41] Hopcroft J E, Karp R M. An n5/2 algorithm for maximum matchings in bipartite graphs. SIAM Journal on Computing, 1973, 2(4):225-231. [42] Zhang X, Lv T, Yang X et al. Structural controllability of complex networks based on preferential matching. PLoS One, 2014, 9(11):Article No. e112039. [43] Goodrich M T, Tamassia R. Algorithm Design:Foundation, Analysis and Internet Examples. John Wiley & Sons, 2006. [44] Wu L, Tang L, Li M et al. The MSS of complex networks with centrality based preference and its application to biomolecular networks. In Proc. the 2016 IEEE International Conference on Bioinformatics and Biomedicine, December 2016, pp.229-234. [45] Pequito S, Kar S, Aguiar A P. On the complexity of the constrained input selection problem for structural linear systems. Automatica, 2015, 62:193-199. [46] Lindmark G, Altafini C. Controllability of complex networks with unilateral inputs. Scientific Reports, 2017, 7:Article No. 1824. [47] Rugh W J, Kailath T. Linear System Theory (2nd edition). Pearson, 1995. [48] Wang L Z, Chen Y Z, Wang W X et al. Physical controllability of complex networks. Scientific Reports, 2017, 7:Article No. 40198. [49] Li G, Tang P, Wen C et al. Boundary constraints for minimum cost control of directed networks. IEEE Transactions on Cybernetics, 2017, 47(12):4196-4207. [50] Czeizler E, Gratie C, Chiu W K et al. Target controllability of linear networks. In Proc. the 14th International Conference on Computational Methods in Systems Biology, September 2016, pp.67-81. [51] Kuhn H W. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 1955, 2(1/2):83-97. [52] Zhang X, Wang H, Lv T. Efficient target control of complex networks based on preferential matching. PLoS One, 2017, 12(4):Article No. e0175375. [53] Liu X, Pan L, Stanley H E et al. Controllability of giant connected components in a directed network. Physical Review E, 2017, 95(4):Article No. 042318. [54] Piao X, Lv T, Zhang X et al. Strategy for community control of complex networks. Physica A:Statistical Mechanics and Its Applications, 2015, 421:98-108. [55] Guo W F, Zhang S W, Wei Z G et al. Constrained target controllability of complex networks. Journal of Statistical Mechanics:Theory and Experiment, 2017, 2017(6):Article No. 063402. [56] Khazanchi R, Dempsey K, Thapa I et al. On identifying and analyzing significant nodes in protein-protein interaction networks. In Proc. the 23rd IEEE International Conference on Data Mining Workshops, December 2013, pp.343-348. [57] Badhwar R, Bagler G. Control of neuronal network in caenorhabditis elegans. PLoS One, 2015, 10(9):Article No. e0139204. [58] Noori H R, Schöttler J, Ercsey-Ravasz M et al. A multiscale cerebral neurochemical connectome of the rat brain. PLoS Biology, 2017, 15(7):Article No. e2002612. [59] Deisseroth K. Circuit dynamics of adaptive and maladaptive behaviour. Nature, 2014, 505(7483):309-317. [60] Kringelbach M L, Jenkinson N, Owen S L et al. Translational principles of deep brain stimulation. Nature Reviews Neuroscience, 2007, 8(8):623-635. [61] Li F, Long T, Lu Y et al. The yeast cell-cycle network is robustly designed. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(14):4781-4786. [62] Davidich M I, Bornholdt S. Boolean network model predicts cell cycle sequence of fission yeast. PLoS One, 2008, 3(2):Article No. e1672. [63] Moes M, Le Béchec A, Crespo I et al. A novel network integrating a miRNA-203/SNAI1 feedback loop which regulates epithelial to mesenchymal transition. PLoS One, 2012, 7(4):Article No. e35440. [64] Krumsiek J, Marr C, Schroeder T et al. Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS One, 2011, 6(8):Article No. e22649. [65] Mendoza L. A network model for the control of the differentiation process in Th cells. Biosystems, 2006, 84(2):101-114. [66] Lee H J, Takemoto N, Kurata H et al. Gata-3 induces T helper cell type 2(Th2) cytokine expression and chromatin remodeling in committed Th1 cells. Journal of Experimental Medicine, 2000, 192(1):105-116. [67] Szabo S J, Kim S T, Costa G L et al. A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell, 2000, 100(6):655-669. [68] Hwang E S, Szabo S J, Schwartzberg P L et al. T helper cell fate specified by kinase-mediated interaction of T-bet with GATA-3. Science, 2005, 307(5708):430-433. [69] Kanhaiya K, Czeizler E, Gratie C et al. Controlling directed protein interaction networks in cancer. Technical Report, Turku Centre for Computer Science, 2017. http://tucs.fi/publications/attachment.php?fname=tKaCzGrPe16a.full.pdf, Nov. 2018. [70] Wu L, Tang L, Li M et al. Biomolecular network controllability with drug binding information. IEEE Transactions on Nano Bioscience, 2017, 16(5):326-332. [71] Jia T, Liu Y Y, Csóka E et al. Emergence of bimodality in controlling complex networks. Nature Communications, 2013, 4:Article No. 2002. [72] Liu X, Pan L. Identifying driver nodes in the human signaling network using structural controllability analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 12(2):467-472. [73] Jia T, Barabási A L. Control capacity and a random sampling method in exploring controllability of complex networks. Scientific Reports, 2013, 3:Article No. 2354. [74] Liu X, Pan L. Detection of driver metabolites in the human liver metabolic network using structural controllability analysis. BMC Systems Biology, 2014, 8(1):Article No. 51. [75] Vinayagam A, Gibson T E, Lee H J et al. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(18):4976-4981. [76] Matsuoka Y, Matsumae H, Katoh M et al. A comprehensive map of the influenza A virus replication cycle. BMC Systems Biology, 2013, 7(1):Article No. 97. [77] Uhart M, Flores G, Bustos D. M. Controllability of proteinprotein interaction phosphorylation-based networks:Participation of the hub 14-3-3 protein family. Scientific Reports, 2016, 6:Article No. 26234. [78] Ravindran V, Sunitha V, Bagler G. Identification of critical regulatory genes in cancer signaling network using controllability analysis. Physica A:Statistical Mechanics and Its Applications, 2017, 474:134-143. [79] Ruths J, Ruths D. Control profiles of complex networks. Science, 2014, 343(6177):1373-1376. [80] Tu C, Rocha R P, Corbetta M et al. Warnings and caveats in brain controllability. Neuroimage, 2017, 176:83-91. [81] Vanunu O, Magger O, Ruppin E et al. Associating genes and protein complexes with disease via network propagation. PLoS Computational Biology, 2010, 6(1):Article No. e1000641. [82] Wang B, Gao L, Gao Y. Control range:A controllabilitybased index for node significance in directed networks. Journal of Statistical Mechanics:Theory and Experiment, 2012, 2012(04):Article No. P04011. [83] Wang B, Gao L, Gao Y et al. Controllability and observability analysis for vertex domination centrality in directed networks. Scientific Reports, 2014, 4:Article No. 5399. |
No related articles found! |
|
|