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2012
J. Wang, Wang, C., Tian, R., Huang, Y. - Z., Lai, X. - S., Lan, X. - Y., Wang, J. - Q., and Chen, H., Sequence variants in the bovine PRDM16 gene associated with body weight in Chinese cattle breeds, vol. 11, pp. 746-755, 2012.
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Trends Cell Biol. 11: 266-273. http://dx.doi.org/10.1016/S0962-8924(01)02001-3 Kajimura S, Seale P, Tomaru T, Erdjument-Bromage H, et al. (2008). Regulation of the brown and white fat gene programs through a PRDM16/CtBP transcriptional complex. Genes Dev. 22: 1397-1409. http://dx.doi.org/10.1101/gad.1666108 Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, et al. (2007). A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315: 525-528. http://dx.doi.org/10.1126/science.1135308 PMid:17185560 Kinameri E, Inoue T, Aruga J, Imayoshi I, et al. (2008). Prdm proto-oncogene transcription factor family expression and interaction with the Notch-Hes pathway in mouse neurogenesis. PLoS One 3: e3859. http://dx.doi.org/10.1371/journal.pone.0003859 PMid:19050759    PMCid:2585159 Komar AA (2007). Silent SNPs: impact on gene function and phenotype. Pharmacogenomics 8: 1075-1080. http://dx.doi.org/10.2217/14622416.8.8.1075 PMid:17716239 Lai X, Lan X, Chen H, Wang X, et al. (2009). A novel SNP of the Hesx1 gene in bovine and its associations with average daily gain. Mol. Biol. Rep. 36: 1677-1681. http://dx.doi.org/10.1007/s11033-008-9368-3 PMid:18853282 Lan XY, Pan CY, Chen H and Zhang CL (2007). An AluI PCR-RFLP detecting a silent allele at the goat POU1F1 locus and its association with production traits. Small Ruminant Res. 73: 8-12. http://dx.doi.org/10.1016/j.smallrumres.2006.10.009 Nedergaard J, Bengtsson T and Cannon B (2007). Unexpected evidence for active brown adipose tissue in adult humans. Am. J. Physiol. Endocrinol. Metab. 293: E444-E452. http://dx.doi.org/10.1152/ajpendo.00691.2006 PMid:17473055 Nei M and Roychoudhury AK (1974). Sampling variances of heterozygosity and genetic distance. Genetics 76: 379-390. PMid:4822472    PMCid:1213072 Oh I, Shimizu H, Satoh T, Okada S, et al. (2006). Identification of nesfatin-1 as a satiety molecule in the hypothalamus. Nature 443: 709-712. http://dx.doi.org/10.1038/nature05162 PMid:17036007 Ren G, Chen H, Zhang LZ, Lan XY, et al. (2010). A coding SNP of LHX4 gene is associated with body weight and body length in bovine. Mol. Biol. Rep. 37: 417-422. http://dx.doi.org/10.1007/s11033-009-9486-6 PMid:19283511 Rhee EJ, Oh KW, Lee WY, Kim SY, et al. (2006). Effects of two common polymorphisms of peroxisome proliferator-activated receptor-gamma gene on metabolic syndrome. Arch. Med. Res. 37: 86-94. http://dx.doi.org/10.1016/j.arcmed.2005.04.008 PMid:16314192 Rosado EL, Bressan J, Martins MF, Cecon PR, et al. (2007). Polymorphism in the PPARgamma2 and beta2-adrenergic genes and diet lipid effects on body composition, energy expenditure and eating behavior of obese women. Appetite 49: 635-643. http://dx.doi.org/10.1016/j.appet.2007.04.003 PMid:17658197 Sauna ZE, Kimchi-Sarfaty C, Ambudkar SV and Gottesman MM (2007). Silent polymorphisms speak: how they affect pharmacogenomics and the treatment of cancer. Cancer Res. 67: 9609-9612. http://dx.doi.org/10.1158/0008-5472.CAN-07-2377 PMid:17942888 Seale P, Kajimura S, Yang W, Chin S, et al. (2007). Transcriptional control of brown fat determination by PRDM16. Cell Metab. 6: 38-54. http://dx.doi.org/10.1016/j.cmet.2007.06.001 PMid:17618855    PMCid:2564846 Seale P, Bjork B, Yang W, Kajimura S, et al. (2008). PRDM16 controls a brown fat/skeletal muscle switch. Nature 454: 961-967. http://dx.doi.org/10.1038/nature07182 PMid:18719582    PMCid:2583329 Walczak R, Tontonoz P and Edward AD (2003). PPAR[gamma] Signaling in Adipose Tissue Development. In: Handbook of Cell Signaling, Academic Press, Burlington, 39-46. Wang YH, Bower NI, Reverter A, Tan SH, et al. (2009). Gene expression patterns during intramuscular fat development in cattle. J. Anim. Sci. 87: 119-130. http://dx.doi.org/10.2527/jas.2008-1082 PMid:18820161 Warner DR, Horn KH, Mudd L, Webb CL, et al. (2007). PRDM16/MEL1: a novel Smad binding protein expressed in murine embryonic orofacial tissue. Biochim. Biophys. Acta 1773: 814-820. http://dx.doi.org/10.1016/j.bbamcr.2007.03.016 PMid:17467076 Yang LL, Hua Q, Liu RK and Yang Z (2009). Association between two common polymorphisms of PPARgamma gene and metabolic syndrome families in a Chinese population. Arch. Med. Res. 40: 89-96. http://dx.doi.org/10.1016/j.arcmed.2008.11.005 PMid:19237017 Zhang C, Wang Y, Chen H, Lan X, et al. (2007). Enhance the efficiency of single-strand conformation polymorphism analysis by short polyacrylamide gel and modified silver staining. Anal. Biochem. 365: 286-287. http://dx.doi.org/10.1016/j.ab.2007.03.023 PMid:17449006
J. Xu, Wang, J., and Chen, B., SLC30A8 (ZnT8) variations and type 2 diabetes in the Chinese Han population, vol. 11, pp. 1592-1598, 2012.
Boutayeb A and Boutayeb S (2005). The burden of non communicable diseases in developing countries. Int. J. Equity Health 4: 2. http://dx.doi.org/10.1186/1475-9276-4-2 PMid:15651987 PMCid:546417   Cauchi S, Proenca C, Choquet H, Gaget S, et al. (2008). Analysis of novel risk loci for type 2 diabetes in a general French population: the D.E.S.I.R. study. J. Mol. Med. 86: 341-348. http://dx.doi.org/10.1007/s00109-007-0295-x PMid:18210030   Chausmer AB (1998). Zinc, insulin and diabetes. J. Am. Coll. Nutr. 17: 109-115. PMid:9550453   Chimienti F, Devergnas S, Favier A and Seve M (2004). Identification and cloning of a beta-cell-specific zinc transporter, ZnT-8, localized into insulin secretory granules. Diabetes 53: 2330-2337. http://dx.doi.org/10.2337/diabetes.53.9.2330 PMid:15331542   Crawford DC and Nickerson DA (2005). Definition and clinical importance of haplotypes. Annu. Rev. Med. 56: 303-320. http://dx.doi.org/10.1146/annurev.med.56.082103.104540 PMid:15660514   Das SK and Elbein SC (2006). The genetic basis of type 2 diabetes. Cell Sci. 2: 100-131.   Frayling TM (2007a). Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat. Rev. Genet. 8: 657-662. http://dx.doi.org/10.1038/nrg2178 PMid:17703236   Frayling TM (2007b). A new era in finding type 2 diabetes genes - the unusual suspects. Diabet. Med. 24: 696-701. http://dx.doi.org/10.1111/j.1464-5491.2007.02172.x PMid:17561964   Horikoshi M, Hara K, Ito C, Shojima N, et al. (2007). Variations in the HHEX gene are associated with increased risk of type 2 diabetes in the Japanese population. Diabetologia 50: 2461-2466. http://dx.doi.org/10.1007/s00125-007-0827-5 PMid:17928989   Kirchhoff K, Machicao F, Haupt A, Schafer SA, et al. (2008). Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated with impaired proinsulin conversion. Diabetologia 51: 597-601. http://dx.doi.org/10.1007/s00125-008-0926-y PMid:18264689   Li Z, Zhang Z, He Z, Tang W, et al. (2009). A partition-ligation-combination-subdivision EM algorithm for haplotype inference with multiallelic markers: update of the SHEsis (http://analysis.bio-x.cn). Cell Res. 19: 519-523. http://dx.doi.org/10.1038/cr.2009.33 PMid:19290020   Livak KJ, Marmaro J and Todd JA (1995). Towards fully automated genome-wide polymorphism screening. Nat. Genet. 9: 341-342. http://dx.doi.org/10.1038/ng0495-341 PMid:7795635   MacDonald PE and Rorsman P (2007). The ins and outs of secretion from pancreatic beta-cells: control of single-vesicle exo- and endocytosis. Physiology 22: 113-121. http://dx.doi.org/10.1152/physiol.00047.2006 PMid:17420302   Morris RW and Kaplan NL (2002). On the advantage of haplotype analysis in the presence of multiple disease susceptibility alleles. Genet. Epidemiol. 23: 221-233. http://dx.doi.org/10.1002/gepi.10200 PMid:12384975   Omori S, Tanaka Y, Takahashi A, Hirose H, et al. (2008). Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes 57: 791-795. http://dx.doi.org/10.2337/db07-0979 PMid:18162508   Owen KR and McCarthy MI (2007). Genetics of type 2 diabetes. Curr. Opin. Genet. Dev. 17: 239-244. http://dx.doi.org/10.1016/j.gde.2007.04.003 PMid:17466512   Palmiter RD and Huang L (2004). Efflux and compartmentalization of zinc by members of the SLC30 family of solute carriers. Pflugers Arch. 447: 744-751. http://dx.doi.org/10.1007/s00424-003-1070-7 PMid:12748859   Sano M, Kuroi N, Nakayama T, Sato N, et al. (2005). Association study of calcitonin-receptor-like receptor gene in essential hypertension. Am. J. Hypertens. 18: 403-408. http://dx.doi.org/10.1016/j.amjhyper.2004.10.016 PMid:15797661   Saxena R, Voight BF, Lyssenko V, Burtt NP, et al. (2007). Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316: 1331-1336. http://dx.doi.org/10.1126/science.1142358 PMid:17463246   Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, et al. (2007). A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316: 1341-1345. http://dx.doi.org/10.1126/science.1142382 PMid:17463248 PMCid:3214617   Shi YY and He L (2005). SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 15: 97-98. http://dx.doi.org/10.1038/sj.cr.7290272 PMid:15740637   Sladek R, Rocheleau G, Rung J, Dina C, et al. (2007). A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445: 881-885. http://dx.doi.org/10.1038/nature05616 PMid:17293876   Staiger H, Machicao F, Stefan N, Tschritter O, et al. (2007). Polymorphisms within novel risk loci for type 2 diabetes determine beta-cell function. PLoS One 2: e832. http://dx.doi.org/10.1371/journal.pone.0000832 PMid:17786204 PMCid:1952072   Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, et al. (2007). A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat. Genet. 39: 770-775. http://dx.doi.org/10.1038/ng2043 PMid:17460697   Xiang X, Ma YT, Fu ZY, Yang YN, et al. (2009). Haplotype analysis of the CYP8A1 gene associated with myocardial infarction. Clin. Appl. Thromb. Hemost. 15: 574-580. http://dx.doi.org/10.1177/1076029608329581 PMid:19147528   Zeggini E (2007). A new era for type 2 diabetes genetics. Diabet. Med. 24: 1181-1186. http://dx.doi.org/10.1111/j.1464-5491.2007.02274.x PMid:17897328 PMCid:2121132   Zeggini E, Weedon MN, Lindgren CM, Frayling TM, et al. (2007). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316: 1336-1341. http://dx.doi.org/10.1126/science.1142364 PMid:17463249
2011
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
X. Chen, Wu, C. - W., Zhong, S. - P., Zeng, F. - R., Zhang, J. - S., Wang, J., and Niu, S. - F., Molecular characterization and structure analysis of RPL10/QM-like protein from the red drum Sciaenops ocellatus (Sciaenidae), vol. 10, pp. 576-587, 2011.
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