Page 80 - GPD-1-2
P. 80

Gene & Protein in Disease                                   Recent advances and challenges of network biology



            94.  Liu ZP, 2018, Towards precise reconstruction of gene   gene expression data. BMC Bioinformatics, 7: 509.
               regulatory networks by data integration.  Quant Biol,      https://doi.org/10.1186/1471-2105-7-509
               6: 113–128.
                                                               106. Ha J, Baladandayuthapani V, Do K, 2015, DINGO:
               https://doi.org/10.1007/s40484-018-0139-4
                                                                  Differential  network  analysis  in  genomics.  Bioinformatics,
            95.  Zhang X, Zhao X, He K, et al., 2012, Inferring gene regulatory   31(21): 3413–3420.
               networks from gene expression data by path consistency      https://doi.org/10.1093/bioinformatics/btv406
               algorithm  based  on  conditional  mutual  information.
               Bioinformatics, 28(1): 98–104.                  107.  Wang  P,  Wang  DJ,  2021,  Gene  differential  co-expression
                                                                  networks based on RNA-seq data, construction and its
               https://doi.org/10.1093/bioinformatics/btr626
                                                                  applications. IEEE/ACM Trans Comput Biol Bioinform.
            96.  Meyer P, Lafitte F, Bontempi G, 2008, minet: A  R/     https://doi.org/10.1109/TCBB.2021.3103280
               Bioconductor  package  for  inferring  large  transcriptional
               networks using mutual information.  BMC  Bioinformatics,   108. Liu XP, Liu ZP, Zhao XM, et al., 2012, Identifying disease
               9: 461.                                            genes and module biomarkers by differential interactions.
                                                                  J Amer Med Inform Assoc, 19(2): 241–248.
               https://doi.org/10.1186/1471-2105-9-461
                                                                  https://doi.org/10.1136/amiajnl-2011-000658
            97.  Wu XQ, Wang WH, Zheng WX, 2012, Inferring topologies
               of complex networks with  hidden variables.  Phys Rev E,   109. Liu XP, Chang X, Liu R,  et al., 2017, Quantifying critical
               86(4): 046106.                                     states of complex diseases using single-sample dynamic
                                                                  network biomarkers. PLoS Comput Biol, 13(7): e1005633.
               https://doi.org/10.1103/PhysRevE.86.046106
                                                                  https://doi.org/10.1371/journal.pcbi.1005633
            98.  Beyer A, Bandyopadhyay S, Ideker T, 2007, Integrating
               physical and genetic maps: From genomes to interaction   110. Tu JJ, Le OY, Yuan Z, et al., 2021, Differential network analysis
               networks. Nat Rev Genet, 8(9): 699–710.            by simultaneously considering changes in gene interactions
                                                                  and gene expression. Bioinformatics, 37(23): 4414–4423.
               https://doi.org/10.1038/nrg2144
                                                                  https://doi.org/10.1093/bioinformatics/btab502
            99.  Ghanbari M, Lasserre J, Vingron M, 2015, Reconstruction
               of gene networks using prior knowledge.  BMC Syst Biol,   111. Hudson NJ, Reverter A, Dalrymple BP, 2009, A differential
               9(1): 84.                                          wiring analysis of expression data correctly identifies the
                                                                  gene containing the causal mutation.  PLoS Comput Biol,
               https://doi.org/10.1186/s12918-015-0233-4
                                                                  5(5): 1000382.
            100. Altarawy D, Eid FE, Heath LS, 2017, PEAK: Integrating
               curated and noisy prior knowledge in gene regulatory      https://doi.org/10.1371/journal.pcbi.1000382
               network inference. J Comput Biol, 24(9): 863–873.   112. Tian WD, Zhang LV, Tasan M,  et al., 2008, Combining
                                                                  guilt-by-association and guilt-by-profiling to predict
               https://doi.org/10.1089/cmb.2016.0199
                                                                  Saccharomyces cerevisiae gene function. Genome Biol, 9: S1.
            101. Jansen  R,  Yu  H,  Greenbaum  D,  et  al.,  2003,  A bayesian
               networks  approach  for  predicting  protein-protein     https://doi.org/10.1186/gb-2008-9-s1-s7
               interactions from genomic data. Science, 302(5644): 449–453.   113. Shin H, Sheu B, Joseph M, et al., 2008, Guilt-by-association
                                                                  feature  selection:  Identifying  biomarkers  from  proteomic
               https://doi.org/10.1126/science.1087361
                                                                  profiles. J Biomed Inform, 41(1): 124–136.
            102. Xiao N, Zhou A, Kempher M, et al., 2022, Disentangling
               direct from indirect relationships in association networks.      https://doi.org/10.1016/j.jbi.2007.04.003
               Proc Natl Acad Sci U S A, 119(2): e2109995119.   114. Kitsak M, Gallos LK, Havlin S, et al., 2010, Identification
                                                                  of influential spreaders in complex networks.  Nat Phys,
               https://doi.org/10.1073/pnas.2109995119
                                                                  6(11): 888–893.
            103. Chowdhury H, Bhattacharyya D, Kalita J,  et al., 2019,
               (Differential) co-expression analysis of gene expression:      https://doi.org/10.1038/NPHYS1746
               A survey of best practices,” IEEE/ACM Trans Comput Biol   115. Wang P, 2021, Statistical identification of important nodes in
               Bioinform, 17(4): 1154–1173.                       biological systems. J Syst Sci Complex, 34(4): 1454–1470.
               https://doi.org/10.1109/TCBB.2019.2893170          https://doi.org/10.1007/s11424-021-0001-2
            104. Tesson B, Breitling R, Jansen R, 2010, DiffCoEx: A simple   116. Lü LY, Zhou T, Zhang QM, et al., 2016, The H-index of a
               and sensitive method to find differentially coexpressed gene   network node and its relation to degree and coreness. Nat
               modules. BMC Bioinformatics, 11: 497.              Commun, 7: 10168.
               https://doi.org/10.1186/1471-2105-11-497           https://doi.org/10.1038/ncomms10168
            105. Watson M, 2006, CoXpress: Differential co-expression in   117. Koschützki D, Schwöbbermeyer H, Schreiber F, 2007,

            Volume 1 Issue 2 (2022)                         14                     https://doi.org/10.36922/gpd.v1i2.101
   75   76   77   78   79   80   81   82   83   84   85