Research graph
References from Prediction of CRISPR-Cas9 on-target activity based on a hybrid neural network. Local targets link to admitted publications; unresolved targets remain external evidence.
CRISPR/Cas9 therapeutics: progress and prospects
10.1038/s41392-023-01309-7 · 2023 · External reference
First-in-human in vivo genome editing via AAV-zinc-finger nucleases for mucopolysaccharidosis I/II and hemophilia B
10.1016/j.ymthe.2022.10.010 · 2022 · External reference
10.1016/j.ggedit.2021.100007
10.1016/j.ggedit.2021.100007 · External reference
10.1002/advs.201902312
10.1002/advs.201902312 · External reference
10.1038/s41467-020-14957-y
10.1038/s41467-020-14957-y · External reference
CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context
10.1038/s41467-022-30515-0 · 2022 · External reference
10.1126/science.aas9129
10.1126/science.aas9129 · External reference
CRISPR-Cas9 off-target predictions using CNN and double CNN: comparative analysis
10.30574/ijsra.2024.12.1.0927 · 2024 · External reference
Benchmarking CRISPR on-target sgRNA design
10.1093/bib/bbx001 · 2018 · External reference
10.1093/bioinformatics/btu743
10.1093/bioinformatics/btu743 · External reference
Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning
2019 · External reference
Deepcrispr: optimized CRISPR guide RNA design by deep learning
10.1186/s13059-018-1459-4 · 2018 · External reference
10.1038/nmeth.3543
10.1038/nmeth.3543 · External reference
10.1038/nbt.3026
10.1038/nbt.3026 · External reference
Predicting E-commerce customer satisfaction: traditional machine learning vs. deep learning approaches
10.1016/j.jretconser.2024.103865 · 2024 · External reference
Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review
10.1093/bib/bbad131 · 2023 · External reference
Crisprpred (SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning
10.1186/s12859-020-3531-9 · 2020 · External reference
Machine learning applications based on SVM classification a review
10.48161/qaj.v1n2a50 · 2021 · External reference
10.1016/j.isprsjprs.2020.12.010
10.1016/j.isprsjprs.2020.12.010 · External reference
Unresolved reference
2017 · External reference
10.1038/nbt.3300
10.1038/nbt.3300 · External reference
10.1016/j.csbj.2023.10.018 · Admitted local publication
10.1016/j.csbj.2021.03.001 · Admitted local publication
Textconvonet: a convolutional neural network based architecture for text classification
10.1007/s10489-022-04221-9 · 2023 · External reference
10.1371/journal.pcbi.1007480
10.1371/journal.pcbi.1007480 · External reference
10.3390/ijms25084429
10.3390/ijms25084429 · External reference
Water quality prediction in the luan river based on 1-drcnn and BiGRU hybrid neural network model
10.3390/w13091273 · 2021 · External reference
Multi-scale convolutional network with channel attention mechanism for rolling bearing fault diagnosis
10.1016/j.measurement.2022.111935 · 2022 · External reference
Unresolved reference
External reference
Unresolved reference
2021 · External reference
10.1126/science.1225829
10.1126/science.1225829 · External reference
10.1126/science.aad5227
10.1126/science.aad5227 · External reference
10.1038/nature16526
10.1038/nature16526 · External reference
10.1038/nature26155
10.1038/nature26155 · External reference
10.1038/s41467-018-05477-x
10.1038/s41467-018-05477-x · External reference
High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities
10.1016/j.cell.2015.11.015 · 2015 · External reference
10.1126/science.1246981
10.1126/science.1246981 · External reference
Deepmens: an ensemble model for predicting sgRNA on-target activity based on multiple features
2024 · External reference
10.1038/s41587-020-0537-9
10.1038/s41587-020-0537-9 · External reference
Induction of apoptosis in cancer cells by bilberry (vaccinium myrtillus) and the anthocyanins
10.1021/jf025781x · 2003 · External reference
10.18653/v1/d16-1146
10.18653/v1/d16-1146 · External reference
Distributed representations of words and phrases and their compositionality
2013 · External reference
10.1371/journal.pcbi.1012340
10.1371/journal.pcbi.1012340 · External reference
Unresolved reference
2016 · External reference
Unresolved reference
2010 · External reference
Dropout: a simple way to prevent neural networks from overfitting
2014 · External reference
rPPG-MAE: self-supervised pretraining with masked autoencoders for remote physiological measurements
2024 · External reference
Unresolved reference
1995 · External reference
Spearman's rank correlation coefficient
2014 · External reference
Analytic posteriors for Pearson's correlation coefficient
10.1111/stan.12111 · 2018 · External reference
Transcrispr: transformer based hybrid model for predicting CRISPR/Cas9 single guide RNA cleavage efficiency
10.1109/tcbb.2022.3201631 · 2022 · External reference
10.1016/j.csbj.2020.01.013 · Admitted local publication
Unresolved reference
External reference
A convolutional neural network using dinucleotide one-hot encoder for identifying DNA N6-methyladenine sites in the rice genome
10.1016/j.neucom.2020.09.056 · 2021 · External reference
Multi-scale attention convolutional neural network for time series classification
10.1016/j.neunet.2021.01.001 · 2021 · External reference
Summarization of scholarly articles using BERT and BiGRU: deep learning-based extractive approach
10.1016/j.jksuci.2023.101739 · 2023 · External reference
Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models
10.1016/j.catena.2023.106953 · 2023 · External reference
Attention is all you need
2017 · External reference
A novel hybrid CNN-SVR for CRISPR/Cas9 guide RNA activity prediction
10.3389/fgene.2019.01303 · 2020 · External reference
