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Items: 13

1.

Human Absorbable MicroRNA Prediction based on an Ensemble Manifold Ranking Model.

Shu J, Chiang K, Zhao D, Cui J.

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2015 Nov;2015:295-300. doi: 10.1109/BIBM.2015.7359697. Epub 2015 Dec 17.

2.

mirDIP 4.1-integrative database of human microRNA target predictions.

Tokar T, Pastrello C, Rossos AEM, Abovsky M, Hauschild AC, Tsay M, Lu R, Jurisica I.

Nucleic Acids Res. 2018 Jan 4;46(D1):D360-D370. doi: 10.1093/nar/gkx1144.

3.

Tiny giants of gene regulation: experimental strategies for microRNA functional studies.

Steinkraus BR, Toegel M, Fulga TA.

Wiley Interdiscip Rev Dev Biol. 2016 May-Jun;5(3):311-62. doi: 10.1002/wdev.223. Epub 2016 Mar 7. Review.

4.

Identification of AGO3-associated miRNAs and computational prediction of their targets in the green alga Chlamydomonas reinhardtii.

Voshall A, Kim EJ, Ma X, Moriyama EN, Cerutti H.

Genetics. 2015 May;200(1):105-21. doi: 10.1534/genetics.115.174797. Epub 2015 Mar 13.

5.

Interferon-alpha competing endogenous RNA network antagonizes microRNA-1270.

Kimura T, Jiang S, Yoshida N, Sakamoto R, Nishizawa M.

Cell Mol Life Sci. 2015 Jul;72(14):2749-61. doi: 10.1007/s00018-015-1875-5. Epub 2015 Mar 7.

6.

MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets.

Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z.

Sci Rep. 2015 Jan 23;5:8004. doi: 10.1038/srep08004.

7.

Improving miRNA-mRNA interaction predictions.

Tabas-Madrid D, Muniategui A, Sánchez-Caballero I, Martínez-Herrera DJ, Sorzano CO, Rubio A, Pascual-Montano A.

BMC Genomics. 2014;15 Suppl 10:S2. doi: 10.1186/1471-2164-15-S10-S2. Epub 2014 Dec 12.

8.

The oncogenic and prognostic potential of eight microRNAs identified by a synergetic regulatory network approach in lung cancer.

Mitra R, Zhao Z.

Int J Comput Biol Drug Des. 2014;7(4):384-93. doi: 10.1504/IJCBDD.2014.066572. Epub 2014 Dec 25.

9.

A tri-component conservation strategy reveals highly confident microRNA-mRNA interactions and evolution of microRNA regulatory networks.

Lin CC, Mitra R, Zhao Z.

PLoS One. 2014 Jul 23;9(7):e103142. doi: 10.1371/journal.pone.0103142. eCollection 2014.

10.
11.

miR-1207-5p and miR-1266 suppress gastric cancer growth and invasion by targeting telomerase reverse transcriptase.

Chen L, Lü MH, Zhang D, Hao NB, Fan YH, Wu YY, Wang SM, Xie R, Fang DC, Zhang H, Hu CJ, Yang SM.

Cell Death Dis. 2014 Jan 30;5:e1034. doi: 10.1038/cddis.2013.553.

12.

RFMirTarget: predicting human microRNA target genes with a random forest classifier.

Mendoza MR, da Fonseca GC, Loss-Morais G, Alves R, Margis R, Bazzan AL.

PLoS One. 2013 Jul 26;8(7):e70153. doi: 10.1371/journal.pone.0070153. Print 2013.

13.

MREdictor: a two-step dynamic interaction model that accounts for mRNA accessibility and Pumilio binding accurately predicts microRNA targets.

Incarnato D, Neri F, Diamanti D, Oliviero S.

Nucleic Acids Res. 2013 Oct;41(18):8421-33. doi: 10.1093/nar/gkt629. Epub 2013 Jul 17.

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