166 related articles for article (PubMed ID: 36289480)
1. BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models.
Zarate OA; Yang Y; Wang X; Wang JP
BMC Bioinformatics; 2022 Oct; 23(1):446. PubMed ID: 36289480
[TBL] [Abstract][Full Text] [Related]
2. A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency.
Liu Y; Fan R; Yi J; Cui Q; Cui C
Comput Biol Med; 2023 Oct; 165():107476. PubMed ID: 37696181
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. TransCrispr: Transformer Based Hybrid Model for Predicting CRISPR/Cas9 Single Guide RNA Cleavage Efficiency.
Wan Y; Jiang Z
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1518-1528. PubMed ID: 36006888
[TBL] [Abstract][Full Text] [Related]
5. CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction.
Li B; Ai D; Liu X
Biomolecules; 2022 Mar; 12(3):. PubMed ID: 35327601
[TBL] [Abstract][Full Text] [Related]
6. CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.
Zhu W; Xie H; Chen Y; Zhang G
Int J Mol Sci; 2024 Apr; 25(8):. PubMed ID: 38674012
[TBL] [Abstract][Full Text] [Related]
7. Improved sgRNA design in bacteria via genome-wide activity profiling.
Guo J; Wang T; Guan C; Liu B; Luo C; Xie Z; Zhang C; Xing XH
Nucleic Acids Res; 2018 Aug; 46(14):7052-7069. PubMed ID: 29982721
[TBL] [Abstract][Full Text] [Related]
8. A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets.
Ham DT; Browne TS; Banglorewala PN; Wilson TL; Michael RK; Gloor GB; Edgell DR
Nat Commun; 2023 Sep; 14(1):5514. PubMed ID: 37679324
[TBL] [Abstract][Full Text] [Related]
9. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action.
Abadi S; Yan WX; Amar D; Mayrose I
PLoS Comput Biol; 2017 Oct; 13(10):e1005807. PubMed ID: 29036168
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. CRISPRlnc: a machine learning method for lncRNA-specific single-guide RNA design of CRISPR/Cas9 system.
Yang Z; Zhang Z; Li J; Chen W; Liu C
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38426328
[TBL] [Abstract][Full Text] [Related]
12. Sequence features associated with the cleavage efficiency of CRISPR/Cas9 system.
Liu X; Homma A; Sayadi J; Yang S; Ohashi J; Takumi T
Sci Rep; 2016 Jan; 6():19675. PubMed ID: 26813419
[TBL] [Abstract][Full Text] [Related]
13. Natural Nucleoside Modifications in Guide RNAs Can Modulate the Activity of the CRISPR-Cas9 System
Prokhorova DV; Vokhtantsev IP; Tolstova PO; Zhuravlev ES; Kulishova LM; Zharkov DO; Stepanov GA
CRISPR J; 2022 Dec; 5(6):799-812. PubMed ID: 36350691
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering.
Noshay JM; Walker T; Alexander WG; Klingeman DM; Romero J; Walker AM; Prates E; Eckert C; Irle S; Kainer D; Jacobson DA
Nucleic Acids Res; 2023 Oct; 51(19):10147-10161. PubMed ID: 37738140
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity.
Xiao LM; Wan YQ; Jiang ZR
BMC Bioinformatics; 2021 Dec; 22(1):589. PubMed ID: 34903170
[TBL] [Abstract][Full Text] [Related]
18. [Application of machine learning in the CRISPR/Cas9 system].
Zhang GS; Yang Y; Zhang LM; Dai XH
Yi Chuan; 2018 Sep; 40(9):704-723. PubMed ID: 30369475
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]