These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

174 related articles for article (PubMed ID: 30443369)

  • 1.
    Sanchez-Garcia R; Segura J; Maluenda D; Carazo JM; Sorzano COS
    IUCrJ; 2018 Nov; 5(Pt 6):854-865. PubMed ID: 30443369
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.
    Zhu Y; Ouyang Q; Mao Y
    BMC Bioinformatics; 2017 Jul; 18(1):348. PubMed ID: 28732461
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A Transfer Learning-Based Classification Model for Particle Pruning in Cryo-Electron Microscopy.
    Li H; Chen G; Gao S; Li J; Wan X; Zhang F
    J Comput Biol; 2022 Oct; 29(10):1117-1131. PubMed ID: 35985012
    [No Abstract]   [Full Text] [Related]  

  • 4. AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images.
    Al-Azzawi A; Ouadou A; Tanner JJ; Cheng J
    BMC Bioinformatics; 2019 Jun; 20(1):326. PubMed ID: 31195977
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules.
    Yao R; Qian J; Huang Q
    Bioinformatics; 2020 Feb; 36(4):1252-1259. PubMed ID: 31584618
    [TBL] [Abstract][Full Text] [Related]  

  • 6. DRPnet: automated particle picking in cryo-electron micrographs using deep regression.
    Nguyen NP; Ersoy I; Gotberg J; Bunyak F; White TA
    BMC Bioinformatics; 2021 Feb; 22(1):55. PubMed ID: 33557750
    [TBL] [Abstract][Full Text] [Related]  

  • 7. MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.
    Sanchez-Garcia R; Segura J; Maluenda D; Sorzano COS; Carazo JM
    J Struct Biol; 2020 Jun; 210(3):107498. PubMed ID: 32276087
    [TBL] [Abstract][Full Text] [Related]  

  • 8. DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.
    Al-Azzawi A; Ouadou A; Max H; Duan Y; Tanner JJ; Cheng J
    BMC Bioinformatics; 2020 Nov; 21(1):509. PubMed ID: 33167860
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Auto3DCryoMap: an automated particle alignment approach for 3D cryo-EM density map reconstruction.
    Al-Azzawi A; Ouadou A; Duan Y; Cheng J
    BMC Bioinformatics; 2020 Dec; 21(Suppl 21):534. PubMed ID: 33371884
    [TBL] [Abstract][Full Text] [Related]  

  • 10. DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.
    Wang F; Gong H; Liu G; Li M; Yan C; Xia T; Li X; Zeng J
    J Struct Biol; 2016 Sep; 195(3):325-336. PubMed ID: 27424268
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs.
    Dhakal A; Gyawali R; Wang L; Cheng J
    bioRxiv; 2023 Oct; ():. PubMed ID: 37961171
    [TBL] [Abstract][Full Text] [Related]  

  • 12. CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.
    Gyawali R; Dhakal A; Wang L; Cheng J
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38860738
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Super-Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM.
    Al-Azzawi A; Ouadou A; Tanner JJ; Cheng J
    Genes (Basel); 2019 Aug; 10(9):. PubMed ID: 31480377
    [TBL] [Abstract][Full Text] [Related]  

  • 14. APPLE picker: Automatic particle picking, a low-effort cryo-EM framework.
    Heimowitz A; Andén J; Singer A
    J Struct Biol; 2018 Nov; 204(2):215-227. PubMed ID: 30134153
    [TBL] [Abstract][Full Text] [Related]  

  • 15. CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs.
    Dhakal A; Gyawali R; Wang L; Cheng J
    Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38407301
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PIXER: an automated particle-selection method based on segmentation using a deep neural network.
    Zhang J; Wang Z; Chen Y; Han R; Liu Z; Sun F; Zhang F
    BMC Bioinformatics; 2019 Jan; 20(1):41. PubMed ID: 30658571
    [TBL] [Abstract][Full Text] [Related]  

  • 17. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM.
    Wagner T; Merino F; Stabrin M; Moriya T; Antoni C; Apelbaum A; Hagel P; Sitsel O; Raisch T; Prumbaum D; Quentin D; Roderer D; Tacke S; Siebolds B; Schubert E; Shaikh TR; Lill P; Gatsogiannis C; Raunser S
    Commun Biol; 2019; 2():218. PubMed ID: 31240256
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net.
    Gyawali R; Dhakal A; Wang L; Cheng J
    bioRxiv; 2024 Mar; ():. PubMed ID: 37873264
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method.
    Fang K; Wang J; Chen Q; Feng X; Qu Y; Shi J; Xu Z
    PLoS One; 2024; 19(4):e0298287. PubMed ID: 38593135
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Exploring applications of crowdsourcing to cryo-EM.
    Bruggemann J; Lander GC; Su AI
    J Struct Biol; 2018 Jul; 203(1):37-45. PubMed ID: 29486249
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.