Jing Zhang

Orcid: 0000-0002-5970-0509

Affiliations:
  • Department of Computer Science, University of California, Irvine, CA, USA
  • Yale University, Department of Molecular Biophysics and Biochemistry, New Haven, CT, USA (former)


According to our database1, Jing Zhang authored at least 27 papers between 2018 and 2024.

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Bibliography

2024
Single-Cell Omics Arena: A Benchmark Study for Large Language Models on Cell Type Annotation Using Single-Cell Data.
CoRR, 2024

scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding.
Briefings Bioinform., 2024

scACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

iMIRACLE: An Iterative Multi-View Graph Neural Network to Model Intercellular Gene Regulation From Spatial Transcriptomic Data.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

iHAST: Integrating Hybrid Attention for Super-Resolution in Spatial Transcriptomics.
Proceedings of the 35th British Machine Vision Conference, 2024

Understanding Transcriptional Regulatory Redundancy by Learnable Global Subset Perturbations.
Proceedings of the Asian Conference on Machine Learning, 2024

2023
iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.
PLoS Comput. Biol., 2023

2022
Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data.
PLoS Comput. Biol., October, 2022

Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series.
J. Comput. Biol., 2022

Translator: A <i>Trans</i>fer <i>L</i>earning Approach to Facilitate Single-Cell <i>AT</i>AC-Seq Data Analysis fr<i>o</i>m <i>R</i>eference Dataset.
J. Comput. Biol., 2022

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network.
IEEE Access, 2022

Unsupervised Multi-Task Learning for 3D Subtomogram Image Alignment, Clustering and Segmentation.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

Deep Active Learning for Cryo-Electron Tomography Classification.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021
Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
PLoS Comput. Biol., 2021

Forest Fire Clustering: Cluster-oriented Label Propagation Clustering and Monte Carlo Verification Inspired by Forest Fire Dynamics.
CoRR, 2021

Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography.
Bioinform., 2021

DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays.
Bioinform., 2021

Unsupervised Domain Alignment Based Open Set Structural Recognition of Macromolecules Captured By Cryo-Electron Tomography.
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

2020
AttPNet: Attention-Based Deep Neural Network for 3D Point Set Analysis.
Sensors, 2020

Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms.
PLoS Comput. Biol., 2020

Epigenome-based splicing prediction using a recurrent neural network.
PLoS Comput. Biol., 2020

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.
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Nat., 2020

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis.
BMC Bioinform., 2020

DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring.
BMC Bioinform., 2020

TopicNet: a framework for measuring transcriptional regulatory network change.
Bioinform., 2020

PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation.
Algorithms, 2020

2018
MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.
Bioinform., 2018


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