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J. L. Li, Wang, L. F., Wang, H. Y., Bai, L. Y., and Yuan, Z. M., High-accuracy splice site prediction based on sequence component and position features, vol. 11, pp. 3432-3451, 2012.
Asa BH, Cheng SO and Sonnenburg S (2008). Support vector machines and kernels for computational biology. PLoS 4: 1-10.   Baten AK, Chang BC, Halgamuge SK and Li J (2006). Splice site identification using probabilistic parameters and SVM classification. BMC Bioinformatics 7 (Suppl 5): S15. PMid:17254299 PMCid:1764471   Baten AK, Halgamuge SK, Chang B and Wickramarachchi N (2007). Biological sequence data preprocessing for classification: A case study in splice site identification. Adv. Neural Netw. 4492: 1221-1230.   Baten AK, Halgamuge SK and Chang BC (2008). Fast splice site detection using information content and feature reduction. BMC Bioinformatics 9 (Suppl 12): S8. PMid:19091031 PMCid:2638148   Burset M, Seledtsov IA and Solovyev VV (2000). Analysis of canonical and non-canonical splice sites in mammalian genomes. Nucleic Acids Res. 28: 4364-4375. PMid:11058137 PMCid:113136   Cai D, Delcher A, Kao B and Kasif S (2000). Modeling splice sites with Bayes networks. Bioinformatics 16: 152-158. PMid:10842737   Chang CC and Lin CJ (2011). LIBSVM: a library for support vector machines. Trans. Intell. Syst. Technol. 2: 278-289.   Chen TM, Lu CC and Li WH (2005). Prediction of splice sites with dependency graphs and their expanded Bayesian networks. Bioinformatics 21: 471-482. PMid:15374869   Crooks GE, Hon G, Chandonia JM and Brenner SE (2004). WebLogo: a sequence logo generator. Genome Res. 14: 1188- 1190. PMid:15173120 PMCid:419797   Davis J and Goadrich M (2006). The Relationship Between Precision-Recall and ROC Curves. In: Proceedings of the 23rd International Conference on Machine Learning (ICML), New York, 233-240.   Durbin R, Eddy S, Krogh A and Mitchison G (1998). Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic Acids Cambridge. Cambridge University Press, Cambridge.   Fawcett T (2003). ROC Graphs: Notes and Practical Considerations for Data Mining Researchers. Technical Report HPL- 2003-4, HP Laboratories, Palo Alto.   Kahn AB, Ryan MC, Liu H, Zeeberg BR, et al. (2007). SpliceMiner: a high-throughput database implementation of the NCBI evidence viewer for microarray splice variant analysis. BMC Bioinformatics 8: 75. PMid:17338820 PMCid:1839109   Mareshi SA, Eslahchi C and Pezechk H (2008). Impact of RNA structure on the prediction of donor and acceptor splice sites. BMC Bioinformatics 7: 297. PMid:16772025 PMCid:1526458   Muller KR, Mika S and Ratsch G (2001). An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12: 181-201. PMid:18244377   Pertea M, Lin X and Salzberg SL (2001). GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res. 29: 1185-1190. PMid:11222768 PMCid:29713   Pollastro P and Rampone S (2002). HS3D, a dataset of Homo sapiens splice regions, and its extraction procedure from a major public database. Int. J. Mod. Phys. C 13: 1105-1117.   Rätsch G and Sonnenburg S (2004). Accurate Splice Site Detection for Caenorhabditis Elegans. In: Kernel Methods in Computational Biology (Schölkopf KT and Vert JP, eds.). MIT Press, Cambridge.   Rätsch G, Sonnenburg S and Schölkopf B (2005). RASE: recognition of alternatively spliced exons in C. elegans. Bioinformatics 21: i369-i377. PMid:15961480   Rätsch G, Sonnenburg S, Srinivasan J, Witte H, et al. (2007). Improving the Caenorhabditis elegans genome annotation using machine learning. PLoS Comput. Biol. 3: e20. PMid:17319737 PMCid:1808025   Reese MG, Eeckman F, Kupl D and Haussler D (1997). Improved splice site detection in Genie. J. Comp. Biol. 4: 311-324. PMid:9278062   Schneider TD and Stephens RM (1990). Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 18: 6097-6100. PMid:2172928 PMCid:332411   Sonnenburg S, Schweikert G, Philips P, Behr J, et al. (2007). Accurate splice site prediction using support vector machines. BMC Bioinformatics 8 (Suppl 10): S7. PMid:18269701 PMCid:2230508   Staden R (1984). Computer methods to locate signals in nucleic acid sequences. Nucleic Acids Res. 12: 505-519. PMid:6364039 PMCid:321067   Sun ZX, Sang LJ and Ju LN (2008). Splice site prediction based on splicing information and motif sequences character. Chin. Sci. Bull. 53: 2298-2306.   Tavares LG, Lopes HS and Lima CRE (2009). Evaluation of weight matrix models in the splice junction recognition problem. Bioinform. Biomed. Workshop 1: 14-19.   Vapnik VN (1995). The Nature of Statistical Learning Theory. Springer Verlag, New York. PMid:8555380   Wang K, Ussery DW and Brunak S (2009). Analysis and prediction of gene splice sites in four Aspergillus genomes. Fungal Genet. Biol. 4: 14-18. PMid:18948220   Zhang QW, Peng QK and Xu T (2009). DNA splice site sequences clustering method for conservativeness analysis. Prog. Nat. Sci. 19: 511-516.   Zhang QW, Peng QK and Zhang Q (2010). Splice sites prediction of human genome using length-variable Markov model and feature selection. Expert Syst. Appl. 37: 2771-2782.   Zhang Y, Chu CH and Chen YX (2006). Splice site prediction using support vector machines with a Beyes kernel. Expert Syst. Appl. 30: 73-81.   Zien A, Rätsch G and Mika S (2000). Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16: 799-19. PMid:11108702
R. L. Sun, Wang, H. Y., Yang, X. Y., Sheng, Z. J., Li, L. M., Wang, L., Wang, Z. G., and Fei, J., Resistance to lipopolysaccharide-induced endotoxic shock in heterozygous Zfp191 gene-knockout mice, vol. 10, pp. 3712-3721, 2011.
Albanese V, Biguet NF, Kiefer H, Bayard E, et al. (2001). Quantitative effects on gene silencing by allelic variation at a tetranucleotide microsatellite. Hum. Mol. Genet. 10: 1785-1792. PMid:11532988   Edelstein LC and Collins T (2005). The SCAN domain family of zinc finger transcription factors. Gene 359: 1-17. PMid:16139965   Halees AS, Leyfer D and Weng Z (2003). PromoSer: A large-scale mammalian promoter and transcription start site identification service. Nucleic Acids Res. 31: 3554-3559. PMid:12824364 PMCid:168956   Han ZG, Zhang QH, Ye M, Kan LX, et al. (1999). Molecular cloning of six novel Kruppel-like zinc finger genes from hematopoietic cells and identification of a novel transregulatory domain KRNB. J. Biol. Chem. 274: 35741-35748. PMid:10585455   Harper J, Yan L, Loureiro RM, Wu I, et al. (2007). Repression of vascular endothelial growth factor expression by the zinc finger transcription factor ZNF24. Cancer Res. 67: 8736-8741. PMid:17875714   Khalfallah O, Faucon-Biguet N, Nardelli J, Meloni R, et al. (2008). Expression of the transcription factor Zfp191 during embryonic development in the mouse. Gene Expr. Patterns 8: 148-154. PMid:18096443   Khalfallah O, Ravassard P, Lagache CS, Fligny C, et al. (2009). Zinc finger protein 191 (ZNF191/Zfp191) is necessary to maintain neural cells as cycling progenitors. Stem Cells 27: 1643-1653. PMid:19544452   Kyriakis JM and Avruch J (2001). Mammalian mitogen-activated protein kinase signal transduction pathways activated by stress and inflammation. Physiol. Rev. 81: 807-869. PMid:11274345   Lee JC, Kassis S, Kumar S, Badger A, et al. (1999). p38 mitogen-activated protein kinase inhibitors-mechanisms and therapeutic potentials. Pharmacol. Ther. 82: 389-397.   Li J, Chen X, Yang H, Wang S, et al. (2006). The zinc finger transcription factor 191 is required for early embryonic development and cell proliferation. Exp. Cell Res. 312: 3990-3998. PMid:17064688   Li J, Chen X, Gong X, Liu Y, et al. (2009). A transcript profiling approach reveals the zinc finger transcription factor ZNF191 is a pleiotropic factor. BMC Genomics 10: 241. PMid:19463170 PMCid:2694838   Lu D, Searles MA and Klug A (2003). Crystal structure of a zinc-finger-RNA complex reveals two modes of molecular recognition. Nature 426: 96-100. PMid:14603324   Mannel DN (2007). Advances in sepsis research derived from animal models. Int. J. Med. Microbiol. 297: 393-400. PMid:17452126   Manthey CL, Wang SW, Kinney SD and Yao Z (1998). SB202190, a selective inhibitor of p38 mitogen-activated protein kinase, is a powerful regulator of LPS-induced mRNAs in monocytes. J. Leukoc. Biol. 64: 409-417. PMid:9738669   Moriyama M, Matsukawa A, Kudoh S, Takahashi T, et al. (2006). The neuropeptide neuromedin U promotes IL-6 production from macrophages and endotoxin shock. Biochem. Biophys. Res. Commun. 341: 1149-1154. PMid:16466693   Noll L, Peterson FC, Hayes PL, Volkman BF, et al. (2008). Heterodimer formation of the myeloid zinc finger 1 SCAN domain and association with promyelocytic leukemia nuclear bodies. Leuk. Res. 32: 1582-1592. PMid:18472161   Prost JF, Negre D, Cornet-Javaux F, Cortay JC, et al. (1999). Isolation, cloning, and expression of a new murine zinc finger encoding gene. Biochim. Biophys. Acta 1447: 278-283.   Remick DG and Ward PA (2005). Evaluation of endotoxin models for the study of sepsis. Shock 24 (Suppl 1): 7-11. PMid:16374366   Roth K, Chen WM and Lin TJ (2008). Positive and negative regulatory mechanisms in high-affinity IgE receptor-mediated mast cell activation. Arch. Immunol. Ther. Exp. 56: 385-399. PMid:19082920   Silvestri C, Narimatsu M, von B, I, Liu Y, et al. (2008). Genome-wide identification of Smad/Foxh1 targets reveals a role for Foxh1 in retinoic acid regulation and forebrain development. Dev. Cell 14: 411-423. PMid:18331719   Sriskandan S and Altmann DM (2008). The immunology of sepsis. J. Pathol. 214: 211-223. PMid:18161754   Tarca AL, Draghici S, Khatri P, Hassan SS, et al. (2009). A novel signaling pathway impact analysis. Bioinformatics 25: 75-82. PMid:18990722 PMCid:2732297   van der Poll T and van Deventer SJ (1999). Cytokines and anticytokines in the pathogenesis of sepsis. Infect. Dis. Clin. North Am. 13: 413-26, ix.   Wang H, Sun R, Liu G, Yao M, et al. (2008). Characterization of the target DNA sequence for the DNA-binding domain of zinc finger protein 191. Acta Biochim. Biophys. Sin. 40: 704-710.   Watanabe E, Hirasawa H, Oda S, Matsuda K, et al. (2005). Extremely high interleukin-6 blood levels and outcome in the critically ill are associated with tumor necrosis factor- and interleukin-1-related gene polymorphisms. Crit. Care Med. 33: 89-97. PMid:15644653