SWI6/YLR182W Literature Guide Help

Other names published for SWI6: PSL8, SDS11, YLR182W

SWI6 - Computational analysis (49)

Results: 1 - 30 of 49 records
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ReferenceOther Genes Addressed
Hansen L, et al.  (2012) Differences in local genomic context of bound and unbound motifs. Gene 506(1):125-34
Contador CA, et al.  (2011) Identification of transcription factors perturbed by the synthesis of high levels of a foreign protein in yeast saccharomyces cerevisiae. Biotechnol Prog 27(4):925-36
Gallo CA, et al.  (2011) Discovering Time-Lagged Rules from Microarray Data using Gene Profile Classifiers. BMC Bioinformatics 12(1):123
Gormley M, et al.  (2011) An integrated framework to model cellular phenotype as a component of biochemical networks. Adv Bioinformatics 2011():608295
Rao AR and Pellegrini M  (2011) Regulation of the yeast metabolic cycle by transcription factors with periodic activities. BMC Syst Biol 5(1):160
Tuglus C and van der Laan MJ  (2011) Repeated measures semiparametric regression using targeted maximum likelihood methodology with application to transcription factor activity discovery. Stat Appl Genet Mol Biol 10(1):Article2
Verdicchio MP and Kim S  (2011) Identifying targets for intervention by analyzing basins of attraction. Pac Symp Biocomput ():350-61
Vohradska E and Vohradsky J  (2011) Virtual mutagenesis of the yeast cyclins genetic network reveals complex dynamics of transcriptional control networks. PLoS One 6(4):e18827
Wang H, et al.  (2011) Yeast cell cycle transcription factors identification by variable selection criteria. Gene 485(2):172-6
Aucher W, et al.  (2010) A Strategy for Interaction Site Prediction between Phospho-binding Modules and their Partners Identified from Proteomic Data. Mol Cell Proteomics 9(12):2745-59
Goh WS, et al.  (2010) Blurring of high-resolution data shows that the effect of intrinsic nucleosome occupancy on transcription factor binding is mostly regional, not local. PLoS Comput Biol 6(1):e1000649
To CC and Vohradsky J  (2010) Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network. FASEB J 24(9):3468-78
Wang G, et al.  (2010) Process-based network decomposition reveals backbone motif structure. Proc Natl Acad Sci U S A 107(23):10478-83
Alberghina L, et al.  (2009) Molecular networks and system-level properties. J Biotechnol 144(3):224-33
Chen T and Li F  (2009) Identifying cell cycle regulators and combinatorial interactions among transcription factors with microarray data and ChIP-chip data. Int J Bioinform Res Appl 5(6):625-46
Huang SS and Fraenkel E  (2009) Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci Signal 2(81):ra40
Lyu S  (2009) Combining boolean method with delay times for determining behaviors of biological networks. Conf Proc IEEE Eng Med Biol Soc 1():4884-7
Nguyen Ba AN, et al.  (2009) NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction. BMC Bioinformatics 10:202
Song M, et al.  (2009) Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfurfural tolerance for ethanologenic yeast. IET Syst Biol 3(3):203
Swamy KB, et al.  (2009) Impact of DNA-binding position variants on yeast gene expression. Nucleic Acids Res 37(21):6991-7001
Xiao Y and Segal MR  (2009) Identification of yeast transcriptional regulation networks using multivariate random forests. PLoS Comput Biol 5(6):e1000414
Holloway DT, et al.  (2008) Classifying transcription factor targets and discovering relevant biological features. Biol Direct 3:22
Qi Y, et al.  (2008) Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions. Genome Res 18(12):1991-2004
Wu WS and Li WH  (2008) Systematic identification of yeast cell cycle transcription factors using multiple data sources. BMC Bioinformatics 9:522
Zhao XM, et al.  (2008) Uncovering signal transduction networks from high-throughput data by integer linear programming. Nucleic Acids Res 36(9):e48
Alarcon T and Tindall MJ  (2007) Modelling cell growth and its modulation of the G1/S transition. Bull Math Biol 69(1):197-214
Braunewell S and Bornholdt S  (2007) Superstability of the yeast cell-cycle dynamics: Ensuring causality in the presence of biochemical stochasticity. J Theor Biol 245(4):638-43
Chen G, et al.  (2007) Clustering of genes into regulons using integrated modeling-COGRIM. Genome Biol 8(1):R4
Ernst J, et al.  (2007) Reconstructing dynamic regulatory maps. Mol Syst Biol 3():74
Holloway DT, et al.  (2007) Machine learning for regulatory analysis and transcription factor target prediction in yeast. Syst Synth Biol 1(1):25-46
Results: 1 - 30 of 49 records
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