Qinhu Zhang
Orcid: 0000-0002-4232-7736
According to our database1,
Qinhu Zhang
authored at least 35 papers
between 2013 and 2024.
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Bibliography
2024
IEEE Trans. Artif. Intell., June, 2024
Briefings Bioinform., January, 2024
Identification of ferroptosis-related lncRNAs for predicting prognosis and immunotherapy response in non-small cell lung cancer.
Future Gener. Comput. Syst., 2024
Proceedings of the International Joint Conference on Neural Networks, 2024
2023
scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data.
BMC Bioinform., December, 2023
DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.
IEEE J. Biomed. Health Informatics, September, 2023
Computational prediction and characterization of cell-type-specific and shared binding sites.
Bioinform., January, 2023
Using Fully Convolutional Network to Locate Transcription Factor Binding Sites Based on DNA Sequence and Conservation Information.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
Predicting the Sequence Specificities of DNA-Binding Proteins by DNA Fine-Tuned Language Model With Decaying Learning Rates.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction.
PLoS Comput. Biol., 2023
In silico prediction methods of self-interacting proteins: an empirical and academic survey.
Frontiers Comput. Sci., 2023
Proceedings of the Applied Intelligence - First International Conference, 2023
2022
DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.
PLoS Comput. Biol., October, 2022
FCNGRU: Locating Transcription Factor Binding Sites by Combing Fully Convolutional Neural Network With Gated Recurrent Unit.
IEEE J. Biomed. Health Informatics, 2022
Predicting In-Vitro DNA-Protein Binding With a Spatially Aligned Fusion of Sequence and Shape.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
A Deep Learning Model for RNA-Protein Binding Preference Prediction Based on Hierarchical LSTM and Attention Network.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
Base-resolution prediction of transcription factor binding signals by a deep learning framework.
PLoS Comput. Biol., 2022
2021
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
Special issue: Advanced Intelligent Computing Theory and Applications in Big Data Era.
Neurocomputing, 2021
Briefings Bioinform., 2021
2020
Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
Predicting in-Vitro Transcription Factor Binding Sites with Deep Embedding Convolution Network.
Proceedings of the Intelligent Computing Theories and Application, 2020
A New Method Combining DNA Shape Features to Improve the Prediction Accuracy of Transcription Factor Binding Sites.
Proceedings of the Intelligent Computing Theories and Application, 2020
Three-Layer Dynamic Transfer Learning Language Model for E. Coli Promoter Classification.
Proceedings of the Intelligent Computing Theories and Application, 2020
2019
High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites.
IEEE ACM Trans. Comput. Biol. Bioinform., 2019
Proceedings of the Intelligent Computing Theories and Application, 2019
2016
Int. J. Mach. Learn. Cybern., 2016
2014
Proceedings of the IEEE International Conference on Information and Automation, 2014
Proceedings of the IEEE International Conference on Information and Automation, 2014
2013
Comparative study of two-layer particle swarm optimization and particle swarm optimization in classification for tumor gene expression data with different dimensionalities.
Proceedings of the 6th International Conference on Biomedical Engineering and Informatics, 2013