191 related articles for article (PubMed ID: 37154091)
1. Predicting cellular responses to complex perturbations in high-throughput screens.
Lotfollahi M; Klimovskaia Susmelj A; De Donno C; Hetzel L; Ji Y; Ibarra IL; Srivatsan SR; Naghipourfar M; Daza RM; Martin B; Shendure J; McFaline-Figueroa JL; Boyeau P; Wolf FA; Yakubova N; Günnemann S; Trapnell C; Lopez-Paz D; Theis FJ
Mol Syst Biol; 2023 Jun; 19(6):e11517. PubMed ID: 37154091
[TBL] [Abstract][Full Text] [Related]
2. scGen predicts single-cell perturbation responses.
Lotfollahi M; Wolf FA; Theis FJ
Nat Methods; 2019 Aug; 16(8):715-721. PubMed ID: 31363220
[TBL] [Abstract][Full Text] [Related]
3. Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.
Kana O; Nault R; Filipovic D; Marri D; Zacharewski T; Bhattacharya S
Patterns (N Y); 2023 Aug; 4(8):100817. PubMed ID: 37602218
[TBL] [Abstract][Full Text] [Related]
4. CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy.
Yuan B; Shen C; Luna A; Korkut A; Marks DS; Ingraham J; Sander C
Cell Syst; 2021 Feb; 12(2):128-140.e4. PubMed ID: 33373583
[TBL] [Abstract][Full Text] [Related]
5. Predicting transcriptional outcomes of novel multigene perturbations with GEARS.
Roohani Y; Huang K; Leskovec J
Nat Biotechnol; 2023 Aug; ():. PubMed ID: 37592036
[TBL] [Abstract][Full Text] [Related]
6. Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.
Yeh CH; Chen ZG; Liou CY; Chen MJ
Bioengineering (Basel); 2023 Aug; 10(9):. PubMed ID: 37760098
[TBL] [Abstract][Full Text] [Related]
7. Modeling interpretable correspondence between cell state and perturbation response with CellCap.
Xu Y; Fleming S; Tegtmeyer M; McCarroll SA; Babadi M
bioRxiv; 2024 Mar; ():. PubMed ID: 38558987
[TBL] [Abstract][Full Text] [Related]
8. Computational Analysis and In silico Predictive Modeling for Inhibitors of PhoP Regulon in S. typhi on High-Throughput Screening Bioassay Dataset.
Kaur H; Ahmad M; Scaria V
Interdiscip Sci; 2016 Mar; 8(1):95-101. PubMed ID: 26298582
[TBL] [Abstract][Full Text] [Related]
9. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.
Mav D; Shah RR; Howard BE; Auerbach SS; Bushel PR; Collins JB; Gerhold DL; Judson RS; Karmaus AL; Maull EA; Mendrick DL; Merrick BA; Sipes NS; Svoboda D; Paules RS
PLoS One; 2018; 13(2):e0191105. PubMed ID: 29462216
[TBL] [Abstract][Full Text] [Related]
10. Stratification and prediction of drug synergy based on target functional similarity.
Yang M; Jaaks P; Dry J; Garnett M; Menden MP; Saez-Rodriguez J
NPJ Syst Biol Appl; 2020 Jun; 6(1):16. PubMed ID: 32487991
[TBL] [Abstract][Full Text] [Related]
11. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.
Jiang Q; Chen S; Chen X; Jiang R
Bioinformatics; 2024 May; 40(5):. PubMed ID: 38625746
[TBL] [Abstract][Full Text] [Related]
12. DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.
Umarov R; Li Y; Arner E
PLoS Comput Biol; 2021 Oct; 17(10):e1009465. PubMed ID: 34610009
[TBL] [Abstract][Full Text] [Related]
13. Deciphering Combinatorial Genetics.
Wong AS; Choi GC; Lu TK
Annu Rev Genet; 2016 Nov; 50():515-538. PubMed ID: 27732793
[TBL] [Abstract][Full Text] [Related]
14. Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq.
Brockway S; Wang G; Jackson JM; Amici DR; Takagishi SR; Clutter MR; Bartom ET; Mendillo ML
Nat Commun; 2020 Nov; 11(1):5722. PubMed ID: 33184288
[TBL] [Abstract][Full Text] [Related]
15. CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators.
Kowalski MH; Wessels HH; Linder J; Choudhary S; Hartman A; Hao Y; Mascio I; Dalgarno C; Kundaje A; Satija R
bioRxiv; 2023 Feb; ():. PubMed ID: 36798324
[TBL] [Abstract][Full Text] [Related]
16. The manatee variational autoencoder model for predicting gene expression alterations caused by transcription factor perturbations.
Yang Y; Seninge L; Wang Z; Oro A; Stuart JM; Ding H
Sci Rep; 2024 May; 14(1):11794. PubMed ID: 38782963
[TBL] [Abstract][Full Text] [Related]
17. TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens.
Muellner MK; Duernberger G; Ganglberger F; Kerzendorfer C; Uras IZ; Schoenegger A; Bagienski K; Colinge J; Nijman SM
BMC Bioinformatics; 2014 Apr; 15():98. PubMed ID: 24712852
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Categorical Matrix Completion With Active Learning for High-Throughput Screening.
Chen J; Hou J; Wong KC
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2261-2270. PubMed ID: 32203025
[TBL] [Abstract][Full Text] [Related]
20. Machine learning for perturbational single-cell omics.
Ji Y; Lotfollahi M; Wolf FA; Theis FJ
Cell Syst; 2021 Jun; 12(6):522-537. PubMed ID: 34139164
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]