Similar articles for PMID: 26710886

Year	Number of Results
2003	1
2007	1
2009	1
2010	1
2013	2
2014	3
2015	16
2016	19
2017	5
2018	9
2019	8
2020	6
2021	9
2022	3
2023	2
2024	0

1

Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors.

Mitra AK, Mukherjee UK, Harding T, Jang JS, Stessman H, Li Y, Abyzov A, Jen J, Kumar S, Rajkumar V, Van Ness B. Mitra AK, et al. Leukemia. 2016 May;30(5):1094-102. doi: 10.1038/leu.2015.361. Epub 2015 Dec 29. Leukemia. 2016. PMID: 26710886

2

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma.

Ghasemi M, Alpsoy S, Türk S, Malkan ÜY, Atakan Ş, Haznedaroğlu İC, Güneş G, Gündüz M, Yılmaz B, Etgül S, Aydın S, Aslan T, Sayınalp N, Aksu S, Demiroğlu H, Özcebe OI, Büyükaşık Y, Göker H. Ghasemi M, et al. Turk J Haematol. 2016 Dec 1;33(4):286-292. doi: 10.4274/tjh.2015.0145. Epub 2016 Apr 18. Turk J Haematol. 2016. PMID: 27095044 Free PMC article.

3

scGen predicts single-cell perturbation responses.

Lotfollahi M, Wolf FA, Theis FJ. Lotfollahi M, et al. Nat Methods. 2019 Aug;16(8):715-721. doi: 10.1038/s41592-019-0494-8. Epub 2019 Jul 29. Nat Methods. 2019. PMID: 31363220

4

Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.

Fan J, Lee HO, Lee S, Ryu DE, Lee S, Xue C, Kim SJ, Kim K, Barkas N, Park PJ, Park WY, Kharchenko PV. Fan J, et al. Genome Res. 2018 Aug;28(8):1217-1227. doi: 10.1101/gr.228080.117. Epub 2018 Jun 13. Genome Res. 2018. PMID: 29898899 Free PMC article.

5

A Machine Learning Model to Predict Survival and Therapeutic Responses in Multiple Myeloma.

Ren L, Xu B, Xu J, Li J, Jiang J, Ren Y, Liu P. Ren L, et al. Int J Mol Sci. 2023 Apr 3;24(7):6683. doi: 10.3390/ijms24076683. Int J Mol Sci. 2023. PMID: 37047654 Free PMC article.

6

Drug response prediction in high-risk multiple myeloma.

Vangsted AJ, Helm-Petersen S, Cowland JB, Jensen PB, Gimsing P, Barlogie B, Knudsen S. Vangsted AJ, et al. Gene. 2018 Feb 20;644:80-86. doi: 10.1016/j.gene.2017.10.071. Epub 2017 Nov 6. Gene. 2018. PMID: 29122646 Clinical Trial.

7

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

Hu Y, Hase T, Li HP, Prabhakar S, Kitano H, Ng SK, Ghosh S, Wee LJ. Hu Y, et al. BMC Genomics. 2016 Dec 22;17(Suppl 13):1025. doi: 10.1186/s12864-016-3317-7. BMC Genomics. 2016. PMID: 28155657 Free PMC article.

8

HelPredictor models single-cell transcriptome to predict human embryo lineage allocation.

Liang P, Zheng L, Long C, Yang W, Yang L, Zuo Y. Liang P, et al. Brief Bioinform. 2021 Nov 5;22(6):bbab196. doi: 10.1093/bib/bbab196. Brief Bioinform. 2021. PMID: 34037706

9

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

Zhang X, Li B, Han H, Song S, Xu H, Hong Y, Yi N, Zhuang W. Zhang X, et al. BMC Cancer. 2018 May 10;18(1):551. doi: 10.1186/s12885-018-4483-6. BMC Cancer. 2018. PMID: 29747599 Free PMC article.

10

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

Ji Z, Ji H. Ji Z, et al. Nucleic Acids Res. 2016 Jul 27;44(13):e117. doi: 10.1093/nar/gkw430. Epub 2016 May 13. Nucleic Acids Res. 2016. PMID: 27179027 Free PMC article.

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