Kim SY - Search Results

1

Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

Kim YR, Kim D, Kim SY. Kim YR, et al. Among authors: kim d, kim sy. Cancer Res Treat. 2019 Apr;51(2):672-684. doi: 10.4143/crt.2018.137. Epub 2018 Aug 10. Cancer Res Treat. 2019. PMID: 30092623 Free PMC article.

2

Topological network analysis of differentially expressed genes in cancer cells with acquired gefitinib resistance.

Lee YS, Hwang SG, Kim JK, Park TH, Kim YR, Myeong HS, Kwon K, Jang CS, Noh YH, Kim SY. Lee YS, et al. Among authors: kim jk, kim sy, kim yr. Cancer Genomics Proteomics. 2015 May-Jun;12(3):153-66. Cancer Genomics Proteomics. 2015. PMID: 25977174

3

Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis.

Lee YS, Hwang SG, Kim JK, Park TH, Kim YR, Myeong HS, Choi JD, Kwon K, Jang CS, Ro YT, Noh YH, Kim SY. Lee YS, et al. Among authors: kim jk, kim sy, kim yr. Tumour Biol. 2016 Feb;37(2):2285-97. doi: 10.1007/s13277-015-4033-7. Epub 2015 Sep 11. Tumour Biol. 2016. PMID: 26361955

4

Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

Kim YR, Kim SY. Kim YR, et al. Among authors: kim sy. J Cancer Res Clin Oncol. 2018 Aug;144(8):1435-1444. doi: 10.1007/s00432-018-2676-7. Epub 2018 May 25. J Cancer Res Clin Oncol. 2018. PMID: 29802456

5

Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach.

Kim YR, Kim YW, Lee SE, Yang HW, Kim SY. Kim YR, et al. Among authors: kim sy, kim yw. Cancers (Basel). 2019 Jan 4;11(1):45. doi: 10.3390/cancers11010045. Cancers (Basel). 2019. PMID: 30621238 Free PMC article.

6

A meta-analysis to identify novel diagnostic and therapeutic targets for Dupuytren's disease.

Park TH, Kim D, Lee YS, Kim SY. Park TH, et al. Among authors: kim d, kim sy. Wound Repair Regen. 2020 Mar;28(2):202-210. doi: 10.1111/wrr.12774. Epub 2019 Nov 27. Wound Repair Regen. 2020. PMID: 31688987

7

Gene Prioritization and Network Topology Analysis of Targeted Genes for Acquired Taxane Resistance by Meta-Analysis.

Kim D, Lee YS, Kim JK, Kim SY. Kim D, et al. Among authors: kim jk, kim sy. Crit Rev Eukaryot Gene Expr. 2019;29(6):581-597. doi: 10.1615/CritRevEukaryotGeneExpr.2019026317. Crit Rev Eukaryot Gene Expr. 2019. PMID: 32422012 Review.

8

Endothelial cell senescence: A machine learning-based meta-analysis of transcriptomic studies.

Park HS, Kim SY. Park HS, et al. Among authors: kim sy. Ageing Res Rev. 2021 Jan;65:101213. doi: 10.1016/j.arr.2020.101213. Epub 2020 Nov 12. Ageing Res Rev. 2021. PMID: 33189866 Review.

9

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.

Park KS, Kim SH, Oh JH, Kim SY. Park KS, et al. Among authors: kim sh, kim sy. Brief Bioinform. 2021 Jul 20;22(4):bbaa336. doi: 10.1093/bib/bbaa336. Brief Bioinform. 2021. PMID: 33341874 Free PMC article.

10

Cross-platform meta-analysis of multiple gene expression profiles identifies novel expression signatures in acquired anthracycline-resistant breast cancer.

Lee YS, Ryu SW, Bae SJ, Park TH, Kwon K, Noh YH, Kim SY. Lee YS, et al. Among authors: kim sy. Oncol Rep. 2015 Apr;33(4):1985-93. doi: 10.3892/or.2015.3810. Epub 2015 Feb 17. Oncol Rep. 2015. PMID: 25695524

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