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 *

108 related articles for article (PubMed ID: 38199209)

  • 1. Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT.
    Luo Y; Chen Y; Xie H; Zhu W; Zhang G
    Comput Biol Med; 2024 Feb; 169():107932. PubMed ID: 38199209
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities.
    Zhang G; Luo Y; Dai X; Dai Z
    Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37775147
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Off-target predictions in CRISPR-Cas9 gene editing using deep learning.
    Lin J; Wong KC
    Bioinformatics; 2018 Sep; 34(17):i656-i663. PubMed ID: 30423072
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications.
    Zhang S; Li X; Lin Q; Wong KC
    Bioinformatics; 2019 Apr; 35(7):1108-1115. PubMed ID: 30169558
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints.
    Chen Q; Chuai G; Zhang H; Tang J; Duan L; Guan H; Li W; Li W; Wen J; Zuo E; Zhang Q; Liu Q
    Nat Commun; 2023 Nov; 14(1):7521. PubMed ID: 37980345
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Amplification-free long-read sequencing reveals unforeseen CRISPR-Cas9 off-target activity.
    Höijer I; Johansson J; Gudmundsson S; Chin CS; Bunikis I; Häggqvist S; Emmanouilidou A; Wilbe M; den Hoed M; Bondeson ML; Feuk L; Gyllensten U; Ameur A
    Genome Biol; 2020 Dec; 21(1):290. PubMed ID: 33261648
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Transformer-based anti-noise models for CRISPR-Cas9 off-target activities prediction.
    Guan Z; Jiang Z
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37068307
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Whole genome analysis of CRISPR Cas9 sgRNA off-target homologies via an efficient computational algorithm.
    Zhou H; Zhou M; Li D; Manthey J; Lioutikova E; Wang H; Zeng X
    BMC Genomics; 2017 Nov; 18(Suppl 9):826. PubMed ID: 29219081
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of CRISPR/Cas9 single guide RNA cleavage efficiency and specificity by attention-based convolutional neural networks.
    Zhang G; Zeng T; Dai Z; Dai X
    Comput Struct Biotechnol J; 2021; 19():1445-1457. PubMed ID: 33841753
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of off-target specificity and cell-specific fitness of CRISPR-Cas System using attention boosted deep learning and network-based gene feature.
    Liu Q; He D; Xie L
    PLoS Comput Biol; 2019 Oct; 15(10):e1007480. PubMed ID: 31658261
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep learning improves the ability of sgRNA off-target propensity prediction.
    Liu Q; Cheng X; Liu G; Li B; Liu X
    BMC Bioinformatics; 2020 Feb; 21(1):51. PubMed ID: 32041517
    [TBL] [Abstract][Full Text] [Related]  

  • 12. DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency.
    Elkayam S; Orenstein Y
    Bioinformatics; 2022 Jun; 38(Suppl 1):i161-i168. PubMed ID: 35758815
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of CRISPR-Cas9 off-target activities with mismatches and indels based on hybrid neural network.
    Yang Y; Li J; Zou Q; Ruan Y; Feng H
    Comput Struct Biotechnol J; 2023; 21():5039-5048. PubMed ID: 37867973
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.
    Sherkatghanad Z; Abdar M; Charlier J; Makarenkov V
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37080758
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Genome Editing with CRISPR-Cas9: Can It Get Any Better?
    Haeussler M; Concordet JP
    J Genet Genomics; 2016 May; 43(5):239-50. PubMed ID: 27210042
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions.
    Peng H; Zheng Y; Zhao Z; Liu T; Li J
    Bioinformatics; 2018 Sep; 34(17):i757-i765. PubMed ID: 30423065
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Optimizing sgRNA length to improve target specificity and efficiency for the GGTA1 gene using the CRISPR/Cas9 gene editing system.
    Matson AW; Hosny N; Swanson ZA; Hering BJ; Burlak C
    PLoS One; 2019; 14(12):e0226107. PubMed ID: 31821359
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of sgRNA on-target activity in bacteria by deep learning.
    Wang L; Zhang J
    BMC Bioinformatics; 2019 Oct; 20(1):517. PubMed ID: 31651233
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computational Tools and Resources for CRISPR/Cas Genome Editing.
    Li C; Chu W; Gill RA; Sang S; Shi Y; Hu X; Yang Y; Zaman QU; Zhang B
    Genomics Proteomics Bioinformatics; 2023 Feb; 21(1):108-126. PubMed ID: 35341983
    [TBL] [Abstract][Full Text] [Related]  

  • 20. R-CRISPR: A Deep Learning Network to Predict Off-Target Activities with Mismatch, Insertion and Deletion in CRISPR-Cas9 System.
    Niu R; Peng J; Zhang Z; Shang X
    Genes (Basel); 2021 Nov; 12(12):. PubMed ID: 34946828
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.