Other names published for MBP1: YDL056W
MBP1 LITERATURE TOPICS
- Curated Literature
- Genetics/Cell Biology
- Nucleic Acid Information
- Gene Product Information
- Related Genes/Proteins
- Research Aids
- Genome-wide Analysis
- Proteome-wide Analysis
- Other Topics
- Additional Information
MBP1 - Computational analysis (55)
| Reference | Other Genes Addressed |
|---|---|
| Hansen L, et al. (2012) Differences in local genomic context of bound and unbound motifs. Gene 506(1):125-34 | |
| Brohee S, et al. (2011) Unraveling networks of co-regulated genes on the sole basis of genome sequences. Nucleic Acids Res 39(15):6340-58 | |
| 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 | |
| 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 | |
| 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 | |
| Babbitt GA (2010) Relaxed selection against accidental binding of transcription factors with conserved chromatin contexts. Gene 466(1-2):43-8 | |
| Chen X, et al. (2010) A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data. Bioinformatics 26(12):i334-42 | |
| 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 | |
| Emmert-Streib F and Dehmer M (2009) Information processing in the transcriptional regulatory network of yeast: fnctional robustness. BMC Syst Biol 3:35 | |
| 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 | |
| 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 | |
| Boorsma A, et al. (2008) Inferring Condition-Specific Modulation of Transcription Factor Activity in Yeast through Regulon-Based Analysis of Genomewide Expression. PLoS ONE 3(9):e3112 | |
| Kundaje A, et al. (2008) A predictive model of the oxygen and heme regulatory network in yeast. PLoS Comput Biol 4(11):e1000224 | |
| Lu CC, et al. (2008) Extracting transcription factor binding sites from unaligned gene sequences with statistical models. BMC Bioinformatics 9 Suppl 12:S7 | |
| Wu WS and Li WH (2008) Systematic identification of yeast cell cycle transcription factors using multiple data sources. BMC Bioinformatics 9:522 | |
| Alarcon T and Tindall MJ (2007) Modelling cell growth and its modulation of the G1/S transition. Bull Math Biol 69(1):197-214 |




