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Items: 1 to 20 of 177

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.

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.

3.

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.

4.

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.

5.

Grape seed proanthocyanidins inhibit proliferation of pancreatic cancer cells by modulating microRNA expression.

Wang W, Zhan L, Guo D, Xiang Y, Tian M, Zhang Y, Wu H, Wei Y, Ma G, Han Z.

Oncol Lett. 2019 Mar;17(3):2777-2787. doi: 10.3892/ol.2019.9887. Epub 2019 Jan 4.

6.

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.

7.

EnDisease: a manually curated database for enhancer-disease associations.

Zeng W, Min X, Jiang R.

Database (Oxford). 2019 Jan 1;2019. pii: baz020. doi: 10.1093/database/baz020.

8.

Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information.

Fan XN, Zhang SW, Zhang SY, Zhu K, Lu S.

BMC Bioinformatics. 2019 Feb 19;20(1):87. doi: 10.1186/s12859-019-2675-y.

9.

A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations.

Xuan Z, Li J, Yu J, Feng X, Zhao B, Wang L.

Genes (Basel). 2019 Feb 8;10(2). pii: E126. doi: 10.3390/genes10020126.

10.

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer.

Xiao Q, Luo J, Liang C, Cai J, Li G, Cao B.

BMC Bioinformatics. 2019 Feb 7;20(1):67. doi: 10.1186/s12859-019-2654-3.

11.

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.

12.

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.

13.

An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy.

Wang CC, Chen X, Yin J, Qu J.

RNA Biol. 2019 Mar;16(3):257-269. doi: 10.1080/15476286.2019.1568820. Epub 2019 Jan 28.

PMID:
30646823
14.

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.

15.

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features.

Zhang X, Wang J, Li J, Chen W, Liu C.

BMC Med Genomics. 2018 Dec 31;11(Suppl 6):120. doi: 10.1186/s12920-018-0436-9.

16.

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.

17.

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.

18.

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.

Zhao H, Kuang L, Feng X, Zou Q, Wang L.

Int J Mol Sci. 2018 Dec 28;20(1). pii: E110. doi: 10.3390/ijms20010110.

19.

Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks.

Fan C, Lei X, Wu FX.

Int J Biol Sci. 2018 Nov 1;14(14):1950-1959. doi: 10.7150/ijbs.28260. eCollection 2018.

20.

Plasma miR-21, miR-155, miR-10b, and Let-7a as the potential biomarkers for the monitoring of breast cancer patients.

Khalighfard S, Alizadeh AM, Irani S, Omranipour R.

Sci Rep. 2018 Dec 19;8(1):17981. doi: 10.1038/s41598-018-36321-3.

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