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Items: 1 to 20 of 148


Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures.

Fu X, Zhu W, Cai L, Liao B, Peng L, Chen Y, Yang J.

Front Genet. 2019 Feb 25;10:119. doi: 10.3389/fgene.2019.00119. eCollection 2019.


Development of species specific putative miRNA and its target prediction tool in wheat (Triticum aestivum L.).

Jaiswal S, Iquebal MA, Arora V, Sheoran S, Sharma P, Angadi UB, Dahiya V, Singh R, Tiwari R, Singh GP, Rai A, Kumar D.

Sci Rep. 2019 Mar 7;9(1):3790. doi: 10.1038/s41598-019-40333-y.


Nucleotide-level Convolutional Neural Networks for Pre-miRNA Classification.

Zheng X, Xu S, Zhang Y, Huang X.

Sci Rep. 2019 Jan 24;9(1):628. doi: 10.1038/s41598-018-36946-4.


Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs.

Ma Y, Yu Z, Han G, Li J, Anh V.

BMC Bioinformatics. 2018 Dec 31;19(Suppl 19):521. doi: 10.1186/s12859-018-2518-2.


Systematic miRNome profiling reveals differential microRNAs in transgenic maize metabolism.

Agapito-Tenfen SZ, Vilperte V, Traavik TI, Nodari RO.

Environ Sci Eur. 2018;30(1):37. doi: 10.1186/s12302-018-0168-7. Epub 2018 Sep 19.


Variable importance-weighted Random Forests.

Liu Y, Zhao H.

Quant Biol. 2017 Dec;5(4):338-351. Epub 2017 Nov 6.


Micro-RNAs involved in cellular proliferation have altered expression profiles in granulosa of young women with diminished ovarian reserve.

Woo I, Christenson LK, Gunewardena S, Ingles SA, Thomas S, Ahmady A, Chung K, Bendikson K, Paulson R, McGinnis LK.

J Assist Reprod Genet. 2018 Oct;35(10):1777-1786. doi: 10.1007/s10815-018-1239-9. Epub 2018 Jul 9.


Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.

Rorbach G, Unold O, Konopka BM.

Sci Rep. 2018 May 15;8(1):7560. doi: 10.1038/s41598-018-25578-3.


Identification and characterization of microRNA in the lung tissue of pigs with different susceptibilities to PCV2 infection.

Zhang P, Wang L, Li Y, Jiang P, Wang Y, Wang P, Kang L, Wang Y, Sun Y, Jiang Y.

Vet Res. 2018 Feb 15;49(1):18. doi: 10.1186/s13567-018-0512-3.


A novel method to identify pre-microRNA in various species knowledge base on various species.

Zhao T, Zhang N, Zhang Y, Ren J, Xu P, Liu Z, Cheng L, Hu Y.

J Biomed Semantics. 2017 Sep 20;8(Suppl 1):30. doi: 10.1186/s13326-017-0143-z.


On the performance of pre-microRNA detection algorithms.

Saçar Demirci MD, Baumbach J, Allmer J.

Nat Commun. 2017 Aug 24;8(1):330. doi: 10.1038/s41467-017-00403-z.


Assessing the Potential of Land Use Modification to Mitigate Ambient NO₂ and Its Consequences for Respiratory Health.

Rao M, George LA, Shandas V, Rosenstiel TN.

Int J Environ Res Public Health. 2017 Jul 10;14(7). pii: E750. doi: 10.3390/ijerph14070750.


Circulating microRNA as candidates for early embryonic viability in cattle.

Pohler KG, Green JA, Moley LA, Gunewardena S, Hung WT, Payton RR, Hong X, Christenson LK, Geary TW, Smith MF.

Mol Reprod Dev. 2017 Aug;84(8):731-743. doi: 10.1002/mrd.22856.


A Review on Recent Computational Methods for Predicting Noncoding RNAs.

Zhang Y, Huang H, Zhang D, Qiu J, Yang J, Wang K, Zhu L, Fan J, Yang J.

Biomed Res Int. 2017;2017:9139504. doi: 10.1155/2017/9139504. Epub 2017 May 3. Review.


Detection and comparison of microRNAs in the caprine mammary gland tissues of colostrum and common milk stages.

Hou J, An X, Song Y, Cao B, Yang H, Zhang Z, Shen W, Li Y.

BMC Genet. 2017 May 2;18(1):38. doi: 10.1186/s12863-017-0498-2.


An improved method for identification of small non-coding RNAs in bacteria using support vector machine.

Barman RK, Mukhopadhyay A, Das S.

Sci Rep. 2017 Apr 6;7:46070. doi: 10.1038/srep46070.


Delineating the impact of machine learning elements in pre-microRNA detection.

Saçar Demirci MD, Allmer J.

PeerJ. 2017 Mar 29;5:e3131. doi: 10.7717/peerj.3131. eCollection 2017.


MicroRNA categorization using sequence motifs and k-mers.

Yousef M, Khalifa W, Acar İE, Allmer J.

BMC Bioinformatics. 2017 Mar 14;18(1):170. doi: 10.1186/s12859-017-1584-1.


The landscape of extreme genomic variation in the highly adaptable Atlantic killifish.

Reid NM, Jackson CE, Gilbert D, Minx P, Montague MJ, Hampton TH, Helfrich LW, King BL, Nacci DE, Aluru N, Karchner SI, Colbourne JK, Hahn ME, Shaw JR, Oleksiak MF, Crawford DL, Warren WC, Whitehead A.

Genome Biol Evol. 2017 Feb 13. doi: 10.1093/gbe/evx023. [Epub ahead of print] No abstract available.


Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset.

Xue B, Lipps D, Devineni S.

PLoS One. 2016 Dec 21;11(12):e0168392. doi: 10.1371/journal.pone.0168392. eCollection 2016.

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