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Items: 1 to 20 of 27

1.

Predicting Gene Ontology Function of Human MicroRNAs by Integrating Multiple Networks.

Deng L, Wang J, Zhang J.

Front Genet. 2019 Jan 29;10:3. doi: 10.3389/fgene.2019.00003. eCollection 2019.

2.

miRNAmotif-A Tool for the Prediction of Pre-miRNAā»Protein Interactions.

Urbanek-Trzeciak MO, Jaworska E, Krzyzosiak WJ.

Int J Mol Sci. 2018 Dec 17;19(12). pii: E4075. doi: 10.3390/ijms19124075.

3.

Identification of miR-200c and miR141-Mediated lncRNA-mRNA Crosstalks in Muscle-Invasive Bladder Cancer Subtypes.

Liu G, Chen Z, Danilova IG, Bolkov MA, Tuzankina IA, Liu G.

Front Genet. 2018 Sep 28;9:422. doi: 10.3389/fgene.2018.00422. eCollection 2018.

4.

Biomarker development for hepatocellular carcinoma early detection: current and future perspectives.

Sengupta S, Parikh ND.

Hepat Oncol. 2017 Oct;4(4):111-122. doi: 10.2217/hep-2017-0019. Epub 2017 Nov 17. Review.

5.

miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.

Pla A, Zhong X, Rayner S.

PLoS Comput Biol. 2018 Jul 13;14(7):e1006185. doi: 10.1371/journal.pcbi.1006185. eCollection 2018 Jul.

6.

Evidence for plant-derived xenomiRs based on a large-scale analysis of public small RNA sequencing data from human samples.

Zhao Q, Liu Y, Zhang N, Hu M, Zhang H, Joshi T, Xu D.

PLoS One. 2018 Jun 27;13(6):e0187519. doi: 10.1371/journal.pone.0187519. eCollection 2018.

7.

Acute Hepatopancreatic Necrosis Disease (AHPND) related microRNAs in Litopenaeus vannamei infected with AHPND-causing strain of Vibrio parahemolyticus.

Zheng Z, Aweya JJ, Wang F, Yao D, Lun J, Li S, Ma H, Zhang Y.

BMC Genomics. 2018 May 8;19(1):335. doi: 10.1186/s12864-018-4728-4.

8.
9.

IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.

Zhao YW, Su ZD, Yang W, Lin H, Chen W, Tang H.

Int J Mol Sci. 2017 Aug 24;18(9). pii: E1838. doi: 10.3390/ijms18091838.

10.

Identification and analysis of brown planthopper-responsive microRNAs in resistant and susceptible rice plants.

Wu Y, Lv W, Hu L, Rao W, Zeng Y, Zhu L, He Y, He G.

Sci Rep. 2017 Aug 18;7(1):8712. doi: 10.1038/s41598-017-09143-y.

11.

Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.

Wang YB, You ZH, Li LP, Huang YA, Yi HC.

Molecules. 2017 Aug 18;22(8). pii: E1366. doi: 10.3390/molecules22081366.

12.

Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients.

Lee S, Jiang X.

PLoS One. 2017 Aug 9;12(8):e0182666. doi: 10.1371/journal.pone.0182666. eCollection 2017.

13.

Exosomes derived from palmitic acid-treated hepatocytes induce fibrotic activation of hepatic stellate cells.

Lee YS, Kim SY, Ko E, Lee JH, Yi HS, Yoo YJ, Je J, Suh SJ, Jung YK, Kim JH, Seo YS, Yim HJ, Jeong WI, Yeon JE, Um SH, Byun KS.

Sci Rep. 2017 Jun 16;7(1):3710. doi: 10.1038/s41598-017-03389-2.

14.

Genome-wide identification and characterization of miRNAome from tomato (Solanum lycopersicum) roots and root-knot nematode (Meloidogyne incognita) during susceptible interaction.

Kaur P, Shukla N, Joshi G, VijayaKumar C, Jagannath A, Agarwal M, Goel S, Kumar A.

PLoS One. 2017 Apr 20;12(4):e0175178. doi: 10.1371/journal.pone.0175178. eCollection 2017.

15.

PAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions.

Chen W, Feng P, Ding H, Lin H.

Sci Rep. 2016 Oct 11;6:35123. doi: 10.1038/srep35123.

16.

Analyzing the miRNA-Gene Networks to Mine the Important miRNAs under Skin of Human and Mouse.

Wu J, Gong H, Bai Y, Zhang W.

Biomed Res Int. 2016;2016:5469371. Epub 2016 Sep 5.

17.

Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach.

Liu B, Liu Y, Huang D.

Biomed Res Int. 2016;2016:8527435. doi: 10.1155/2016/8527435. Epub 2016 Aug 25.

18.
19.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

Zhang J, Ju Y, Lu H, Xuan P, Zou Q.

Int J Genomics. 2016;2016:7604641. doi: 10.1155/2016/7604641. Epub 2016 Jul 13.

20.

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.

Wang X, Yan R, Song J.

Sci Rep. 2016 Mar 22;6:23510. doi: 10.1038/srep23510.

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