Dan Zhao

Orcid: 0000-0003-0195-6031

Affiliations:
  • Tsinghua University, Institute for Interdisciplinary Information Sciences, Beijing, China


According to our database1, Dan Zhao authored at least 13 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
MolSHAP: Interpreting Quantitative Structure-Activity Relationships Using Shapley Values of R-Groups.
J. Chem. Inf. Model., 2024

2023
DeepRCI: predicting RNA-chromatin interactions via deep learning with multi-omics data.
Quant. Biol., September, 2023

Improving comparative analyses of Hi-C data via contrastive self-supervised learning.
Briefings Bioinform., July, 2023

Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning.
Nat. Mac. Intell., April, 2023

2022
Predicting the protein-ligand affinity from molecular dynamics trajectories.
CoRR, 2022

Boosting single-cell gene regulatory network reconstruction via bulk-cell transcriptomic data.
Briefings Bioinform., 2022

KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021
Full-length ribosome density prediction by a multi-input and multi-output model.
PLoS Comput. Biol., 2021

Modeling gene regulatory networks using neural network architectures.
Nat. Comput. Sci., 2021

Riboexp: an interpretable reinforcement learning framework for ribosome density modeling.
Briefings Bioinform., 2021

2020
A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories.
Nat. Mach. Intell., 2020

MONN: A Multi-objective Neural Network for Predicting Pairwise Non-covalent Interactions and Binding Affinities Between Compounds and Proteins.
Proceedings of the Research in Computational Molecular Biology, 2020

2019
ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.
Bioinform., 2019


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