Publications
Found 3 results
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“imDC: an ensemble learning method for imbalanced classification with miRNA data”, vol. 14, pp. 123-133, 2015.
, “Production of hGFAP-DsRed transgenic Guangxi Bama mini-pigs via somatic cell nuclear transfer”, vol. 14, pp. 16285-16296, 2015.
, “Benchmark comparison of ab initio microRNA identification methods and software”, vol. 11, pp. 4525-4538, 2012.
, Batuwita R and Palade V (2009). microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25: 989-995.
http://dx.doi.org/10.1093/bioinformatics/btp107
PMid:19233894
Bentwich I, Avniel A, Karov Y, Aharonov R, et al. (2005). Identification of hundreds of conserved and nonconserved human microRNAs. Nat. Genet. 37: 766-770.
http://dx.doi.org/10.1038/ng1590
PMid:15965474
Borchert GM, Lanier W and Davidson BL (2006). RNA polymerase III transcribes human microRNAs. Nat. Struct. Mol. Biol. 13: 1097-1101.
http://dx.doi.org/10.1038/nsmb1167
PMid:17099701
Brennecke J, Hipfner DR, Stark A, Russell RB, et al. (2003). bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113: 25-36.
http://dx.doi.org/10.1016/S0092-8674(03)00231-9
Carrington JC and Ambros V (2003). Role of microRNAs in plant and animal development. Science 301: 336-338.
http://dx.doi.org/10.1126/science.1085242
PMid:12869753
Friedlander MR, Chen W, Adamidi C, Maaskola J, et al. (2008). Discovering microRNAs from deep sequencing data using miRDeep. Nat. Biotechnol. 26: 407-415.
http://dx.doi.org/10.1038/nbt1394
PMid:18392026
Hackenberg M, Sturm M, Langenberger D, Falcon-Perez JM, et al. (2009). miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res. 37: W68-W76.
http://dx.doi.org/10.1093/nar/gkp347
PMid:19433510 PMCid:2703919
Hofacker IL (2003). Vienna RNA secondary structure server. Nucleic Acids Res. 31: 3429-3431.
http://dx.doi.org/10.1093/nar/gkg599
PMid:12824340 PMCid:169005
Huang JC, Babak T, Corson TW, Chua G, et al. (2007). Using expression profiling data to identify human microRNA targets. Nat. Methods 4: 1045-1049.
http://dx.doi.org/10.1038/nmeth1130
PMid:18026111
Huang Y, Zou Q, Tang SM, Wang LG, et al. (2010). Computational identification and characteristics of novel microRNAs from the silkworm (Bombyx mori L.). Mol. Biol. Rep. 37: 3171-3176.
http://dx.doi.org/10.1007/s11033-009-9897-4
PMid:19823945
Huang Y, Shen XJ, Zou Q, Wang SP, et al. (2011a). Biological functions of microRNAs: a review. J. Physiol. Biochem. 67: 129-139.
http://dx.doi.org/10.1007/s13105-010-0050-6
PMid:20981514
Huang Y, Zou Q, Wang SP, Tang SM, et al. (2011b). The discovery approaches and detection methods of microRNAs. Mol. Biol. Rep. 38: 4125-4135.
http://dx.doi.org/10.1007/s11033-010-0532-1
PMid:21107708
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.
http://dx.doi.org/10.1093/nar/gkm368
PMid:17553836 PMCid:1933124
Kumar S, Ansari FA and Scaria V (2009). Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features. Virol. J. 6: 129.
http://dx.doi.org/10.1186/1743-422X-6-129
PMid:19691855 PMCid:2743665
Lee Y, Ahn C, Han J, Choi H, et al. (2003). The nuclear RNase III Drosha initiates microRNA processing. Nature 425: 415-419.
http://dx.doi.org/10.1038/nature01957
PMid:14508493
Li PW, Lu XY, Li CZ, Fang J, et al. (2007). Advances in the study of plant microRNAs. Yi Chuan 29: 283-288.
http://dx.doi.org/10.1360/yc-007-0283
PMid:17369147
Lim LP, Lau NC, Weinstein EG, Abdelhakim A, et al. (2003). The microRNAs of Caenorhabditis elegans. Genes Dev.
http://dx.doi.org/10.1101/gad.1074403
Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, et al. (2000). The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403: 901-906.
http://dx.doi.org/10.1038/35002607
PMid:10706289
Ruby JG, Jan C, Player C, Axtell MJ, et al. (2006). Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell 127: 1193-1207.
http://dx.doi.org/10.1016/j.cell.2006.10.040
PMid:17174894
Sankoff D, Kruskal JB, Mainville S and Cedergren RJ (1983). Fast Algorithms to Determine RNA Secondary Structures Containing Multiple Loops. In: Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison (Sankoff D and Kruskal JB, eds.). Chapter 3. Addison-Wesley, Reading, 93-120.
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.
http://dx.doi.org/10.1186/1471-2105-6-267
PMid:16274478 PMCid:1315341
Wang X, Zhang J, Li F, Gu J, et al. (2005). MicroRNA identification based on sequence and structure alignment. Bioinformatics 21: 3610-3614.
http://dx.doi.org/10.1093/bioinformatics/bti562
PMid:15994192
Wu Y, Wei B, Liu H, Li T, et al. (2011). MiRPara: a SVM-based software tool for prediction of most probable microRNA
Genetics and Molecular Research 11 (4): 4525-4538 (2012) ©FUNPEC-RP www.funpecrp.com.br
L.L. Hu et al. 4538 coding regions in genome scale sequences. BMC Bioinformatics 12: 107.
Xue C, Li F, He T, Liu GP, et al. (2005). Classification of real and pseudo microRNA precursors using local structuresequence features and support vector machine. BMC Bioinformatics 6: 310.
http://dx.doi.org/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.
http://dx.doi.org/10.1093/bioinformatics/btl094
PMid:16543277
Zeng Y, Yi R and Cullen BR (2005). Recognition and cleavage of primary microRNA precursors by the nuclear processing enzyme Drosha. EMBO J. 24: 138-148.
http://dx.doi.org/10.1038/sj.emboj.7600491
PMid:15565168 PMCid:544904
Zou Q, Zhao T, Liu Y and Guo M (2009). Predicting RNA secondary structure based on the class information and Hopfield network. Comput. Biol. Med. 39: 206-214.
http://dx.doi.org/10.1016/j.compbiomed.2008.12.010
PMid:19215914
Zou Q, Lin C, Liu XY, Han YP, et al. (2011). Novel representation of RNA secondary structure used to improve prediction algorithms. Genet. Mol. Res. 10: 1986-1998.
http://dx.doi.org/10.4238/vol10-3gmr1181
PMid:21948761
Zuker M (1989a). Computer prediction of RNA structure. Methods Enzymol. 180: 262-288.
http://dx.doi.org/10.1016/0076-6879(89)80106-5
Zuker M (1989b). On finding all suboptimal foldings of an RNA molecule. Science 244: 48-52.
http://dx.doi.org/10.1126/science.2468181
PMid:2468181
Zuker M (2003). Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31: 3406-3415.
http://dx.doi.org/10.1093/nar/gkg595
PMid:12824337 PMCid:169194
Zuker M and Stiegler P (1981). Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9: 133-148.
http://dx.doi.org/10.1093/nar/9.1.133
PMid:6163133 PMCid:326673