SWI4/YER111C Literature Guide Help

Other names published for SWI4: ART1, YER111C

SWI4 - Computational analysis (61)

Results: 1 - 30 of 61 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
Navlakha S, et al.  (2012) A Network-based Approach for Predicting Missing Pathway Interactions. PLoS Comput Biol 8(8):e1002640
Bosis E, et al.  (2011) A simple yeast-based strategy to identify host cellular processes targeted by bacterial effector proteins. PLoS One 6(11):e27698
Dikicioglu D, et al.  (2011) How yeast re-programmes its transcriptional profile in response to different nutrient impulses. BMC Syst Biol 5(1):148
Gallo CA, et al.  (2011) Discovering Time-Lagged Rules from Microarray Data using Gene Profile Classifiers. BMC Bioinformatics 12(1):123
Gordan R, et al.  (2011) Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights. Genome Biol 12(12):R125
Gormley M, et al.  (2011) An integrated framework to model cellular phenotype as a component of biochemical networks. Adv Bioinformatics 2011():608295
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
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
Babbitt GA  (2010) Relaxed selection against accidental binding of transcription factors with conserved chromatin contexts. Gene 466(1-2):43-8
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
Joshi A, et al.  (2010) Characterizing regulatory path motifs in integrated networks using perturbational data. Genome Biol 11(3):R32
Sirbu A, et al.  (2010) Cross-platform microarray data normalisation for regulatory network inference. PLoS One 5(11):e13822
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
Jothi R, et al.  (2009) Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture. Mol Syst Biol 5:294
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
Swamy KB, et al.  (2009) Impact of DNA-binding position variants on yeast gene expression. Nucleic Acids Res 37(21):6991-7001
Vu TT and Vohradsky J  (2009) Inference of active transcriptional networks by integration of gene expression kinetics modeling and multisource data. Genomics 93(5):426-33
Wang Y, et al.  (2009) Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucleic Acids Res 37(18):5943-58
Xiao Y and Segal MR  (2009) Identification of yeast transcriptional regulation networks using multivariate random forests. PLoS Comput Biol 5(6):e1000414
Ye C, et al.  (2009) Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast. PLoS Comput Biol 5(3):e1000311
Lu CC, et al.  (2008) Extracting transcription factor binding sites from unaligned gene sequences with statistical models. BMC Bioinformatics 9 Suppl 12:S7
Pu S, et al.  (2008) Local coherence in genetic interaction patterns reveals prevalent functional versatility. Bioinformatics 24(20):2376-83
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
Results: 1 - 30 of 61 records
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