Open Access
| Issue |
BIO Web Conf.
Volume 240, 2026
The 2026 International Conference on Biomedicine, Neuroscience and Biostatistics (ICBNB 2026)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 5 | |
| Section | Biomedicine, Neuroscience and Biostatistics | |
| DOI | https://doi.org/10.1051/bioconf/202624001003 | |
| Published online | 24 June 2026 | |
- A. Jasieniecka and I. Domingues, CRISPR-Cas9 and Its Bioinformatics Tools: A Systematic Review. Curr. Issues Mol. Biol. 47, 5, p. 307, Apr. (2025) doi: https://doi.org/10.3390/cimb47050307. [Google Scholar]
- M. Naeem and O. S. Alkhnbashi, Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects. Int. J. Mol. Sci. 24, 7, pp. 6261–6261, Mar. (2023), doi: https://doi.org/10.3390/ijms24076261. [Google Scholar]
- P. Saraswat and R. Ranjan, Unlocking the potential of CRISPR tools and databases for precision genome editing. Front. Plant Sci. 16, Jul. (2025), doi: https://doi.org/10.3389/fpls.2025.1563711. [Google Scholar]
- L. Zhou, X. Zeng, Y. Yang, R. Li, and Z. Zhao, Applications and Prospects of CRISPR/Cas9 Technology in the Breeding of Major Tropical Crops. Plants. 13, 23, p. 3388, Dec. (2024), doi: https://doi.org/10.3390/plants13233388. [Google Scholar]
- L. Hoberecht, P. Perampalam, A. Lun, and J.-P. Fortin, A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies. Nat. Commun. 13, 1, p. 6568, Nov. (2022), doi: https://doi.org/10.1038/s41467-022-34320-7. [Google Scholar]
- Anamul Hoque Emtiaj, R. H. Rafi, M. A. Nayeem, and M. S. Rahman, CRISMER: A transformer-based Interpretable Deep Learning Approach for Genome-wide CRISPR Cas-9 Off-Target Prediction and Optimization, ResearchGate, May 08 (2025).URL: https://www.researchgate.net/publication/391579208_CRISMER_A_transformer-based_Interpretable_Deep_Learning_Approach_for_Genome-wide_CRISPR_Cas-9_Off-Target_Prediction_and_Optimization [Google Scholar]
- M. Cao, A. Brennan, C. M. Lee, S. Park, and G. Bao, Deep Learning Based Models for CRISPR/Cas Off‐ Target Prediction. Small Methods, Jun. (2025). doi: https://doi.org/10.1002/smtd.202500122. [Google Scholar]
- W. Du et al., A versatile CRISPR/Cas9 system off-target prediction tool using language model. Commun. Biol. 8, 1, Jun. (2025). doi: https://doi.org/10.1038/s42003-025-08275-6. [Google Scholar]
- M. Luo, J. Wang, Z. Dong, C. Wang, and G. Lu, CRISPR-Cas9 sgRNA design and outcome assessment: Bioinformatics tools and aquaculture applications. Aquac. Fish. 7, 2, pp. 121–130, Mar. (2022). doi: https://doi.org/10.1016/j.aaf.2021.10.002. [Google Scholar]
- H. Fu, C. Shan, F. Kang, L. Yu, Z. Li, and Y. Yin, CRISPR-GRANT: a cross-platform graphical analysis tool for high-throughput CRISPR-based genome editing evaluation. BMC Bioinform. 24, 1, May (2023). doi: https://doi.org/10.1186/s12859-023-05333-w. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.