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Q. Xu, Gai, P. Y., Lv, H. L., Li, G. R., Liu, X. Y., Xu, Q., Gai, P. Y., Lv, H. L., Li, G. R., Liu, X. Y., Xu, Q., Gai, P. Y., Lv, H. L., Li, G. R., and Liu, X. Y., Association of MMP3 genotype with susceptibility to frozen shoulder: a case-control study in a Chinese Han population, vol. 15, p. -, 2016.
Q. Xu, Gai, P. Y., Lv, H. L., Li, G. R., Liu, X. Y., Xu, Q., Gai, P. Y., Lv, H. L., Li, G. R., Liu, X. Y., Xu, Q., Gai, P. Y., Lv, H. L., Li, G. R., and Liu, X. Y., Association of MMP3 genotype with susceptibility to frozen shoulder: a case-control study in a Chinese Han population, vol. 15, p. -, 2016.
Q. Xu, Gai, P. Y., Lv, H. L., Li, G. R., Liu, X. Y., Xu, Q., Gai, P. Y., Lv, H. L., Li, G. R., Liu, X. Y., Xu, Q., Gai, P. Y., Lv, H. L., Li, G. R., and Liu, X. Y., Association of MMP3 genotype with susceptibility to frozen shoulder: a case-control study in a Chinese Han population, vol. 15, p. -, 2016.
Y. Guan, Yan, L. H., Liu, X. Y., Zhu, X. Y., Wang, S. Z., Chen, L. M., Guan, Y., Yan, L. H., Liu, X. Y., Zhu, X. Y., Wang, S. Z., and Chen, L. M., Correlation of the TCF7L2 (rs7903146) polymorphism with an enhanced risk of type 2 diabetes mellitus: a meta-analysis, vol. 15, p. -, 2016.
Y. Guan, Yan, L. H., Liu, X. Y., Zhu, X. Y., Wang, S. Z., Chen, L. M., Guan, Y., Yan, L. H., Liu, X. Y., Zhu, X. Y., Wang, S. Z., and Chen, L. M., Correlation of the TCF7L2 (rs7903146) polymorphism with an enhanced risk of type 2 diabetes mellitus: a meta-analysis, vol. 15, p. -, 2016.
J. N. Zhang, Yi, S. H., Zhang, X. H., Liu, X. Y., Mao, Q., Li, S. Q., Xiong, W. H., Qiu, Y. M., Chen, T., and Ge, J. W., Association of p53 Arg72Pro and MDM2 SNP309 polymorphisms with glioma, vol. 11, pp. 3618-3628, 2012.
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MDM2 309 T/G polymorphism is associated with colorectal cancer risk especially in Asians: a meta-analysis. Med. Oncol. 28: 981-985. PMid:20503107   Gu J, Liu Y, Kyritsis AP and Bondy ML (2009). Molecular epidemiology of primary brain tumors. Neurotherapeutics 6: 427-435. PMid:19560733   Haupt Y, Maya R, Kazaz A and Oren M (1997). Mdm2 promotes the rapid degradation of p53. Nature 387: 296-299. PMid:9153395   Hunter SB, Abbott K, Varma VA, Olson JJ, et al. (1995). Reliability of differential PCR for the detection of EGFR and MDM2 gene amplification in DNA extracted from FFPE glioma tissue. J. Neuropathol. Exp. Neurol. 54: 57-64. PMid:7815080   Idbaih A, Boisselier B, Marie Y, Sanson M, et al. (2008). Influence of MDM2 SNP309 alone or in combination with the TP53 R72P polymorphism in oligodendroglial tumors. Brain Res. 1198: 16-20. PMid:18262501   Jeong BS, Hu W, Belyi V, Rabadan R, et al. (2010). 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TP53 codon 72 polymorphism in susceptibility, overall survival, and adjuvant therapy response of gliomas. Cancer Genet. Cytogenet. 180: 14-19. PMid:18068527   Liu L, Wang K, Zhu ZM and Shao JH (2011). Associations between P53 Arg72Pro and development of digestive tract cancers: a meta-analysis. Arch. Med. Res. 42: 60-69. PMid:21376265   Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, et al. (2007). The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114: 97-109. PMid:17618441 PMCid:1929165   Malmer B, Feychting M, Lonn S, Ahlbom A, et al. (2005). p53 Genotypes and risk of glioma and meningioma. Cancer Epidemiol. Biomarkers Prev. 14: 2220-2223. PMid:16172235   Malmer BS, Feychting M, Lonn S, Lindstrom S, et al. (2007). Genetic variation in p53 and ATM haplotypes and risk of glioma and meningioma. J. Neurooncol. 82: 229-237. PMid:17151932   Marin MC, Jost CA, Brooks LA, Irwin MS, et al. (2000). A common polymorphism acts as an intragenic modifier of mutant p53 behaviour. Nat. Genet. 25: 47-54. PMid:10802655   Minelli C, Thompson JR, Abrams KR, Thakkinstian A, et al. (2008). How should we use information about HWE in the meta-analyses of genetic association studies? Int. J. Epidemiol. 37: 136-146. PMid:18037675   Ohgaki H, Dessen P, Jourde B, Horstmann S, et al. (2004). Genetic pathways to glioblastoma: a population-based study. Cancer Res. 64: 6892-6899. PMid:15466178   Oliner JD, Pietenpol JA, Thiagalingam S, Gyuris J, et al. (1993). Oncoprotein MDM2 conceals the activation domain of tumour suppressor p53. Nature 362: 857-860. PMid:8479525   Parhar P, Ezer R, Shao Y, Allen JC, et al. (2005). Possible association of p53 codon 72 polymorphism with susceptibility to adult and pediatric high-grade astrocytomas. Brain Res. Mol. Brain Res. 137: 98-103. PMid:15950766   Pinto GR, Yoshioka FK, Silva RL, Clara CA, et al. (2008). Prognostic value of TP53 Pro47Ser and Arg72Pro single nucleotide polymorphisms and the susceptibility to gliomas in individuals from Southeast Brazil. Genet. Mol. Res. 7: 207-216. PMid:18393224   Rajaraman P, Wang SS, Rothman N, Brown MM, et al. (2007). Polymorphisms in apoptosis and cell cycle control genes and risk of brain tumors in adults. Cancer Epidemiol. Biomarkers Prev. 16: 1655-1661. PMid:17684142   Shete S, Hosking FJ, Robertson LB, Dobbins SE, et al. (2009). Genome-wide association study identifies five susceptibility loci for glioma. Nat. Genet. 41: 899-904. PMid:19578367   Suzuki SO and Iwaki T (2000). Amplification and overexpression of mdm2 gene in ependymomas. Mod. Pathol. 13: 548-553. PMid:10824927   Tsuiki H, Nishi T, Takeshima H, Yano S, et al. (2007). Single nucleotide polymorphism 309 affects murin-double-minute 2 protein expression but not glioma tumorigenesis. Neurol. Med. Chir. 47: 203-208.   Wang LE, Bondy ML, Shen H, El-Zein R, et al. (2004). Polymorphisms of DNA repair genes and risk of glioma. Cancer Res. 64: 5560-5563. PMid:15313891   Wrensch M, Fisher JL, Schwartzbaum JA, Bondy M, et al. (2005). The molecular epidemiology of gliomas in adults. Neurosurg. Focus 19: E5. PMid:16398469   Wrensch M, Jenkins RB, Chang JS, Yeh RF, et al. (2009). Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat. Genet. 41: 905-908. PMid:19578366 PMCid:2923561   Yang M, Guo Y, Zhang X, Miao X, et al. (2007). Interaction of P53 Arg72Pro and MDM2 T309G polymorphisms and their associations with risk of gastric cardia cancer. Carcinogenesis 28: 1996-2001. PMid:17638920   Zhou Y, Li N, Zhuang W, Liu GJ, et al. (2007). P53 codon 72 polymorphism and gastric cancer: a meta-analysis of the literature. Int. J. Cancer 121: 1481-1486. PMid:17546594
P. Xuan, Guo, M. Z., Wang, J., Wang, C. Y., Liu, X. Y., and Liu, Y., Genetic algorithm-based efficient feature selection for classification of pre-miRNAs, vol. 10, pp. 588-603, 2011.
Bartel DP (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281-297. doi:10.1016/S0092-8674(04)00045-5 Batuwita R and Palade V (2009). microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25: 989-995. doi:10.1093/bioinformatics/btp107 PMid:19233894 Berezikov E, Guryev V, van de Belt J, Wienholds E, et al. (2005). Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120: 21-24. doi:10.1016/j.cell.2004.12.031 PMid:15652478 Bushati N and Cohen SM (2007). microRNA functions. Annu. Rev. Cell Dev. Biol. 23: 175-205. doi:10.1146/annurev.cellbio.23.090506.123406 PMid:17506695 Chang DT, Wang CC and Chen JW (2008). Using a kernel density estimation based classifier to predict species-specific microRNA precursors. BMC Bioinformatics 9 (Suppl 12): S2. doi:10.1186/1471-2105-9-S12-S2 PMid:19091019    PMCid:2638167 Chatterjee S and Grosshans H (2009). Active turnover modulates mature microRNA activity in Caenorhabditis elegans. Nature 461: 546-549. doi:10.1038/nature08349 PMid:19734881 Fera D, Kim N, Shiffeldrim N, Zorn J, et al. (2004). RAG: RNA-As-Graphs web resource. BMC Bioinformatics 5: 88. doi:10.1186/1471-2105-5-88 PMid:15238163    PMCid:471545 Freyhult E, Gardner PP and Moulton V (2005). A comparison of RNA folding measures. BMC Bioinformatics 6: 241. doi:10.1186/1471-2105-6-241 PMid:16202126    PMCid:1274297 Gan HH, Fera D, Zorn J, Shiffeldrim N, et al. (2004). RAG: RNA-As-Graphs database - concepts, analysis, and features. Bioinformatics 20: 1285-1291. doi:10.1093/bioinformatics/bth084 PMid:14962931 Griffiths-Jones S, Saini HK, van Dongen S and Enright AJ (2008). miRBase: tools for microRNA genomics. Nucleic Acids Res. 36: D154-D158. doi:10.1093/nar/gkm952 PMid:17991681    PMCid:2238936 Hofacker IL, Fontana W, Stadler PF, Bonhoeffer LS, et al. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte fur Chemie/Chemical Monthly 125: 167-188. Jiang P, Wu H, Wang W, Ma W, et al. (2007). MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res. 35: W339-W344. doi:10.1093/nar/gkm368 PMid:17553836    PMCid:1933124 Moulton V, Zuker M, Steel M, Pointon R, et al. (2000). Metrics on RNA secondary structures. J. Comput. Biol. 7: 277-292. doi:10.1089/10665270050081522 PMid:10890402 Nam JW, Shin KR, Han J, Lee Y, et al. (2005). Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res. 33: 3570-3581. doi:10.1093/nar/gki668 PMid:15987789    PMCid:1159118 Ng KL and Mishra SK (2007). De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics 23: 1321-1330. doi:10.1093/bioinformatics/btm026 PMid:17267435 Quinlan JR (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo. Schultes EA, Hraber PT and LaBean TH (1999). Estimating the contributions of selection and self-organization in RNA secondary structure. J. Mol. Evol. 49: 76-83. doi:10.1007/PL00006536 PMid:10368436 Seffens W and Digby D (1999). mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. Nucleic Acids Res. 27: 1578-1584. doi:10.1093/nar/27.7.1578 PMid:10075987    PMCid:148359 Sewer A, Paul N, Landgraf P, Aravin A, et al. (2005). Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 6: 267. doi:10.1186/1471-2105-6-267 PMid:16274478    PMCid:1315341 Xue C, Li F, He T, Liu GP, et al. (2005). Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 6: 310. doi:10.1186/1471-2105-6-310 PMid:16381612    PMCid:1360673 Yousef M, Nebozhyn M, Shatkay H, Kanterakis S, et al. (2006). Combining multi-species genomic data for microRNA identification using a naive Bayes classifier. Bioinformatics 22: 1325-1334. doi:10.1093/bioinformatics/btl094 PMid:16543277 Yousef M, Jung S, Showe LC and Showe MK (2008). Learning from positive examples when the negative class is undetermined - microRNA gene identification. Algorithms Mol. Biol. 3: 2. doi:10.1186/1748-7188-3-2 PMid:18226233    PMCid:2248178 Zhang BH, Pan XP, Cox SB, Cobb GP, et al. (2006). Evidence that miRNAs are different from other RNAs. Cell Mol. Life Sci. 63: 246-254. doi:10.1007/s00018-005-5467-7 PMid:16395542
J. Wang, Liu, X. Y., and Yang, Y. Q., Novel NKX2-5 mutations responsible for congenital heart disease, vol. 10, pp. 2905-2915, 2011.
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