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Items: 16

1.

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.

2.

iTextMine: integrated text-mining system for large-scale knowledge extraction from the literature.

Ren J, Li G, Ross K, Arighi C, McGarvey P, Rao S, Cowart J, Madhavan S, Vijay-Shanker K, Wu CH.

Database (Oxford). 2018 Jan 1;2018. doi: 10.1093/database/bay128.

3.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis.

Hu Y, Dingerdissen H, Gupta S, Kahsay R, Shanker V, Wan Q, Yan C, Mazumder R.

Comput Biol Med. 2018 Dec 1;103:183-197. doi: 10.1016/j.compbiomed.2018.10.021. Epub 2018 Oct 22.

PMID:
30384176
4.

Murine Oviductosomes (OVS) microRNA profiling during the estrous cycle: Delivery of OVS-borne microRNAs to sperm where miR-34c-5p localizes at the centrosome.

Fereshteh Z, Schmidt SA, Al-Dossary AA, Accerbi M, Arighi C, Cowart J, Song JL, Green PJ, Choi K, Yoo S, Martin-DeLeon PA.

Sci Rep. 2018 Oct 31;8(1):16094. doi: 10.1038/s41598-018-34409-4.

5.

Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer.

Chiu AM, Mitra M, Boymoushakian L, Coller HA.

Sci Rep. 2018 Aug 7;8(1):11807. doi: 10.1038/s41598-018-29992-5.

6.

InDePTH: detection of hub genes for developing gene expression networks under anticancer drug treatment.

Koido M, Tani Y, Tsukahara S, Okamoto Y, Tomida A.

Oncotarget. 2018 Jun 26;9(49):29097-29111. doi: 10.18632/oncotarget.25624. eCollection 2018 Jun 26.

7.

Transfer learning for biomedical named entity recognition with neural networks.

Giorgi JM, Bader GD.

Bioinformatics. 2018 Dec 1;34(23):4087-4094. doi: 10.1093/bioinformatics/bty449.

8.

PubRunner: A light-weight framework for updating text mining results.

Anekalla KR, Courneya JP, Fiorini N, Lever J, Muchow M, Busby B.

Version 2. F1000Res. 2017 May 2 [revised 2017 Jan 1];6:612. doi: 10.12688/f1000research.11389.2. eCollection 2017.

9.

Deep learning with word embeddings improves biomedical named entity recognition.

Habibi M, Weber L, Neves M, Wiegandt DL, Leser U.

Bioinformatics. 2017 Jul 15;33(14):i37-i48. doi: 10.1093/bioinformatics/btx228.

10.

Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis.

Balderas-Martínez YI, Rinaldi F, Contreras G, Solano-Lira H, Sánchez-Pérez M, Collado-Vides J, Selman M, Pardo A.

Database (Oxford). 2017 Jan 1;2017. doi: 10.1093/database/bax030.

11.

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries.

Raja K, Patrick M, Gao Y, Madu D, Yang Y, Tsoi LC.

Int J Genomics. 2017;2017:6213474. doi: 10.1155/2017/6213474. Epub 2017 Feb 26. Review.

12.

Improving chemical disease relation extraction with rich features and weakly labeled data.

Peng Y, Wei CH, Lu Z.

J Cheminform. 2016 Oct 7;8:53. doi: 10.1186/s13321-016-0165-z. eCollection 2016.

13.

Extracting microRNA-gene relations from biomedical literature using distant supervision.

Lamurias A, Clarke LA, Couto FM.

PLoS One. 2017 Mar 6;12(3):e0171929. doi: 10.1371/journal.pone.0171929. eCollection 2017.

14.

Analysis of Protein Phosphorylation and Its Functional Impact on Protein-Protein Interactions via Text Mining of the Scientific Literature.

Wang Q, Ross KE, Huang H, Ren J, Li G, Vijay-Shanker K, Wu CH, Arighi CN.

Methods Mol Biol. 2017;1558:213-232. doi: 10.1007/978-1-4939-6783-4_10.

15.

Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts.

Roy S, Curry BC, Madahian B, Homayouni R.

BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):350.

16.

Function and regulation of microRNA-31 in development and disease.

Stepicheva NA, Song JL.

Mol Reprod Dev. 2016 Aug;83(8):654-74. doi: 10.1002/mrd.22678. Epub 2016 Aug 2. Review.

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