Yi-Heng Zhu
Orcid: 0000-0002-3857-1533
According to our database1,
Yi-Heng Zhu
authored at least 13 papers
between 2019 and 2024.
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Bibliography
2024
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction.
Briefings Bioinform., January, 2024
2023
Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction.
Comput. Biol. Medicine, November, 2023
2022
Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction.
PLoS Comput. Biol., December, 2022
TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction.
Genom. Proteom. Bioinform., October, 2022
MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.
Briefings Bioinform., 2022
2021
TargetDBP+: Enhancing the Performance of Identifying DNA-Binding Proteins via Weighted Convolutional Features.
J. Chem. Inf. Model., 2021
Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.
Briefings Bioinform., 2021
Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.
Briefings Bioinform., 2021
Briefings Bioinform., 2021
2020
TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network.
J. Chem. Inf. Model., 2020
2019
DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines.
J. Chem. Inf. Model., 2019
DeepTF: Accurate Prediction of Transcription Factor Binding Sites by Combining Multi-scale Convolution and Long Short-Term Memory Neural Network.
Proceedings of the Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 2019