431 related articles for article (PubMed ID: 33119742)
1. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
Chlis NK; Rausch L; Brocker T; Kranich J; Theis FJ
Nucleic Acids Res; 2020 Nov; 48(20):11335-11346. PubMed ID: 33119742
[TBL] [Abstract][Full Text] [Related]
2. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
[TBL] [Abstract][Full Text] [Related]
3. Use of SuperCT for Enhanced Characterization of Single-Cell Transcriptomic Profiles.
Zhong J; Lin W
Methods Mol Biol; 2020; 2117():169-177. PubMed ID: 31960378
[TBL] [Abstract][Full Text] [Related]
4. A practical guide to intelligent image-activated cell sorting.
Isozaki A; Mikami H; Hiramatsu K; Sakuma S; Kasai Y; Iino T; Yamano T; Yasumoto A; Oguchi Y; Suzuki N; Shirasaki Y; Endo T; Ito T; Hiraki K; Yamada M; Matsusaka S; Hayakawa T; Fukuzawa H; Yatomi Y; Arai F; Di Carlo D; Nakagawa A; Hoshino Y; Hosokawa Y; Uemura S; Sugimura T; Ozeki Y; Nitta N; Goda K
Nat Protoc; 2019 Aug; 14(8):2370-2415. PubMed ID: 31278398
[TBL] [Abstract][Full Text] [Related]
5. An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
Hennig H; Rees P; Blasi T; Kamentsky L; Hung J; Dao D; Carpenter AE; Filby A
Methods; 2017 Jan; 112():201-210. PubMed ID: 27594698
[TBL] [Abstract][Full Text] [Related]
6. Image3C, a multimodal image-based and label-independent integrative method for single-cell analysis.
Accorsi A; Box AC; Peuß R; Wood C; Sánchez Alvarado A; Rohner N
Elife; 2021 Jul; 10():. PubMed ID: 34286692
[TBL] [Abstract][Full Text] [Related]
7. Label-free cell cycle analysis for high-throughput imaging flow cytometry.
Blasi T; Hennig H; Summers HD; Theis FJ; Cerveira J; Patterson JO; Davies D; Filby A; Carpenter AE; Rees P
Nat Commun; 2016 Jan; 7():10256. PubMed ID: 26739115
[TBL] [Abstract][Full Text] [Related]
8. Diagnostic Potential of Imaging Flow Cytometry.
Doan M; Vorobjev I; Rees P; Filby A; Wolkenhauer O; Goldfeld AE; Lieberman J; Barteneva N; Carpenter AE; Hennig H
Trends Biotechnol; 2018 Jul; 36(7):649-652. PubMed ID: 29395345
[TBL] [Abstract][Full Text] [Related]
9. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.
López-García G; Jerez JM; Franco L; Veredas FJ
PLoS One; 2020; 15(3):e0230536. PubMed ID: 32214348
[TBL] [Abstract][Full Text] [Related]
10. Cell type prioritization in single-cell data.
Skinnider MA; Squair JW; Kathe C; Anderson MA; Gautier M; Matson KJE; Milano M; Hutson TH; Barraud Q; Phillips AA; Foster LJ; La Manno G; Levine AJ; Courtine G
Nat Biotechnol; 2021 Jan; 39(1):30-34. PubMed ID: 32690972
[TBL] [Abstract][Full Text] [Related]
11. PXPermute reveals staining importance in multichannel imaging flow cytometry.
Shetab Boushehri S; Kornivetc A; Winter DJE; Kazeminia S; Essig K; Schmich F; Marr C
Cell Rep Methods; 2024 Feb; 4(2):100715. PubMed ID: 38412831
[TBL] [Abstract][Full Text] [Related]
12. Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.
Mukhopadhyay R; Chandel P; Prasad K; Chakraborty U
Methods; 2024 May; 225():62-73. PubMed ID: 38490594
[TBL] [Abstract][Full Text] [Related]
13. Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis.
Mochalova EN; Kotov IA; Lifanov DA; Chakraborti S; Nikitin MP
Biotechnol Bioeng; 2022 Feb; 119(2):626-635. PubMed ID: 34750809
[TBL] [Abstract][Full Text] [Related]
14. Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry.
Li Y; Mahjoubfar A; Chen CL; Niazi KR; Pei L; Jalali B
Sci Rep; 2019 Jul; 9(1):11088. PubMed ID: 31366998
[TBL] [Abstract][Full Text] [Related]
15. Single-Cell Transcriptome Analysis of T Cells.
Van Der Byl W; Rizzetto S; Samir J; Cai C; Eltahla AA; Luciani F
Methods Mol Biol; 2019; 2048():155-205. PubMed ID: 31396939
[TBL] [Abstract][Full Text] [Related]
16. Clustering Single-Cell Expression Data Using Random Forest Graphs.
Pouyan MB; Nourani M
IEEE J Biomed Health Inform; 2017 Jul; 21(4):1172-1181. PubMed ID: 28113735
[TBL] [Abstract][Full Text] [Related]
17. Implementing machine learning methods for imaging flow cytometry.
Ota S; Sato I; Horisaki R
Microscopy (Oxf); 2020 Apr; 69(2):61-68. PubMed ID: 32115658
[TBL] [Abstract][Full Text] [Related]
18. Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx.
Steen CB; Liu CL; Alizadeh AA; Newman AM
Methods Mol Biol; 2020; 2117():135-157. PubMed ID: 31960376
[TBL] [Abstract][Full Text] [Related]
19. Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning.
Ye X; Zhang W; Futamura Y; Sakurai T
Cells; 2020 Aug; 9(9):. PubMed ID: 32825786
[TBL] [Abstract][Full Text] [Related]
20. SCITO-seq: single-cell combinatorial indexed cytometry sequencing.
Hwang B; Lee DS; Tamaki W; Sun Y; Ogorodnikov A; Hartoularos GC; Winters A; Yeung BZ; Nazor KL; Song YS; Chow ED; Spitzer MH; Ye CJ
Nat Methods; 2021 Aug; 18(8):903-911. PubMed ID: 34354295
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]