BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

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]
    of 10.