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Items: 1 to 20 of 411

1.

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

Li X, Lin Y, Gu C, Yang J.

BMC Syst Biol. 2019 Apr 5;13(Suppl 2):26. doi: 10.1186/s12918-019-0696-9.

2.

Identification of lncRNA competitively regulated subpathways in myocardial infarction.

Wu X, Sun L, Wang Z.

Exp Ther Med. 2019 Apr;17(4):3041-3046. doi: 10.3892/etm.2019.7320. Epub 2019 Feb 26.

3.

Elevated Plasma microRNA-105-5p Level in Patients With Idiopathic Parkinson's Disease: A Potential Disease Biomarker.

Yang Z, Li T, Cui Y, Li S, Cheng C, Shen B, Le W.

Front Neurosci. 2019 Mar 18;13:218. doi: 10.3389/fnins.2019.00218. eCollection 2019.

4.

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

Liang C, Yu S, Luo J.

PLoS Comput Biol. 2019 Apr 1;15(4):e1006931. doi: 10.1371/journal.pcbi.1006931. eCollection 2019 Apr.

5.

LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.

Wang L, You ZH, Chen X, Li YM, Dong YN, Li LP, Zheng K.

PLoS Comput Biol. 2019 Mar 27;15(3):e1006865. doi: 10.1371/journal.pcbi.1006865. eCollection 2019 Mar.

6.

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods.

Zhang H, Liang Y, Han S, Peng C, Li Y.

Int J Mol Sci. 2019 Mar 14;20(6). pii: E1284. doi: 10.3390/ijms20061284.

7.

Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature.

Dai HJ, Wang CK, Chang NW, Huang MS, Jonnagaddala J, Wang FD, Hsu WL.

Database (Oxford). 2019 Jan 1;2019. pii: baz030. doi: 10.1093/database/baz030.

8.

A Novel Neighborhood-Based Computational Model for Potential MiRNA-Disease Association Prediction.

Liu Y, Li X, Feng X, Wang L.

Comput Math Methods Med. 2019 Jan 17;2019:5145646. doi: 10.1155/2019/5145646. eCollection 2019.

9.
10.

Comprehensive Investigation of miRNome Identifies Novel Candidate miRNA-mRNA Interactions Implicated in T-Cell Acute Lymphoblastic Leukemia.

Dawidowska M, Jaksik R, Drobna M, Szarzyńska-Zawadzka B, Kosmalska M, Sędek Ł, Machowska L, Lalik A, Lejman M, Ussowicz M, Kałwak K, Kowalczyk JR, Szczepański T, Witt M.

Neoplasia. 2019 Mar;21(3):294-310. doi: 10.1016/j.neo.2019.01.004. Epub 2019 Feb 11.

11.

Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features.

Wang Y, Ru J, Jiang Y, Zhang J.

Sci Rep. 2019 Feb 6;9(1):1521. doi: 10.1038/s41598-018-38048-7.

12.

Drug resistance-related microRNAs in osteosarcoma: Translating basic evidence into therapeutic strategies.

Chen R, Wang G, Zheng Y, Hua Y, Cai Z.

J Cell Mol Med. 2019 Apr;23(4):2280-2292. doi: 10.1111/jcmm.14064. Epub 2019 Feb 5. Review.

13.

Integrating random walk and binary regression to identify novel miRNA-disease association.

Niu YW, Wang GH, Yan GY, Chen X.

BMC Bioinformatics. 2019 Jan 28;20(1):59. doi: 10.1186/s12859-019-2640-9.

14.

Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples.

Che K, Guo M, Wang C, Liu X, Chen X.

Genes (Basel). 2019 Jan 24;10(2). pii: E80. doi: 10.3390/genes10020080.

15.

3D Mammary Epithelial Cell Models: A Goldmine of DCIS Biomarkers and Morphogenetic Mechanisms.

Rossetti S, Sacchi N.

Cancers (Basel). 2019 Jan 23;11(2). pii: E130. doi: 10.3390/cancers11020130.

16.

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases.

Zhao C, Zhang Y, Popel AS.

Int J Mol Sci. 2019 Jan 19;20(2). pii: E421. doi: 10.3390/ijms20020421. Review.

17.

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.

Qu J, Chen X, Sun YZ, Zhao Y, Cai SB, Ming Z, You ZH, Li JQ.

Mol Ther Nucleic Acids. 2019 Mar 1;14:274-286. doi: 10.1016/j.omtn.2018.12.002. Epub 2018 Dec 13.

18.

MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association.

Jiang L, Ding Y, Tang J, Guo F.

Front Genet. 2018 Dec 10;9:618. doi: 10.3389/fgene.2018.00618. eCollection 2018.

19.

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association.

Jiang L, Xiao Y, Ding Y, Tang J, Guo F.

BMC Genomics. 2018 Dec 31;19(Suppl 10):911. doi: 10.1186/s12864-018-5273-x.

20.

FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases.

Sun Y, Zhu Z, You ZH, Zeng Z, Huang ZA, Huang YA.

BMC Syst Biol. 2018 Dec 31;12(Suppl 9):121. doi: 10.1186/s12918-018-0664-9.

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