Zhen-Hao Guo
Orcid: 0009-0007-9764-3529
According to our database1,
Zhen-Hao Guo
authored at least 21 papers
between 2019 and 2024.
Collaborative distances:
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Bibliography
2024
Briefings Bioinform., January, 2024
2023
scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data.
BMC Bioinform., December, 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
In silico prediction methods of self-interacting proteins: an empirical and academic survey.
Frontiers Comput. Sci., 2023
2022
DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.
PLoS Comput. Biol., October, 2022
Base-resolution prediction of transcription factor binding signals by a deep learning framework.
PLoS Comput. Biol., 2022
2021
Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.
BMC Bioinform., 2021
MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.
Briefings Bioinform., 2021
Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network Representation.
Proceedings of the Intelligent Computing Theories and Application, 2021
2020
iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.
PLoS Comput. Biol., 2020
RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.
BMC Bioinform., 2020
A Novel Computational Method for Predicting LncRNA-Disease Associations from Heterogeneous Information Network with SDNE Embedding Model.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Highly Efficient Biomolecular Network Representation Model for Predicting Drug-Disease Associations.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
Inferring Drug-miRNA Associations by Integrating Drug SMILES and MiRNA Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting Drug-Target Interactions by Node2vec Node Embedding in Molecular Associations Network.
Proceedings of the Intelligent Computing Theories and Application, 2020
2019
In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining LSTM Network Model and Wavelet Transform for Predicting Self-interacting Proteins.
Proceedings of the Intelligent Computing Theories and Application, 2019