Jin Zhang

Orcid: 0000-0003-1688-5547

Affiliations:
  • Northwestern Polytechnical University, Department of Intelligent Science and Technology, School of Automation, Xi'an, China


According to our database1, Jin Zhang authored at least 23 papers between 2020 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Toward Trustworthy Multi-View Representation With Fine-Grained Explainability Embeddings.
IEEE Trans. Medical Imaging, February, 2026

Mutual learning for joint disease detection and severity prediction reveals multimodal pathogenesis for neurodegenerative disorders.
Bioinform., 2026

2025
Mutual-assistance learning for trustworthy biomarker discovery and disease prediction.
Briefings Bioinform., March, 2025

Trustworthy causal biomarker discovery: a multiomics brain imaging genetics-based approach.
Bioinform., 2025

Predicting MCI Conversion Status Using Baseline Neuroimaging Scans and Genetics Variations.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2025

2024
Identification of Genetic Risk Factors Based on Disease Progression Derived From Longitudinal Brain Imaging Phenotypes.
IEEE Trans. Medical Imaging, March, 2024

Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.
Briefings Bioinform., March, 2024

Disease Progression Prediction Incorporating Genotype-Environment Interactions: A Longitudinal Neurodegenerative Disorder Study.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

Disentangling Disease-sensitive Multimodal Neuroimaging Phenotypes and Related Genetic Factors: A Multimodal Study of ADNI Cohort.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2024

Identification of disease-related genetic variants and imaging factors leveraging summary statistics.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2024

A Co-contrastive Learning Method to Fuse Multi-modal Phenotypes and Identify Genetic Risk Variations.
Proceedings of the 15th ACM International Conference on Bioinformatics, 2024

2023
inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics.
Genom. Proteom. Bioinform., April, 2023

A multi-task SCCA method for brain imaging genetics and its application in neurodegenerative diseases.
Comput. Methods Programs Biomed., April, 2023

Adaptive structured sparse multiview canonical correlation analysis for multimodal brain imaging association identification.
Sci. China Inf. Sci., April, 2023

Identification of Disease-Sensitive Brain Imaging Phenotypes and Genetic Factors Using GWAS Summary Statistics.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Identifying Main and Epistasis Effects of Genetic Variations on Neuroimaging Phenotypes Using Effective Feature Interaction Learning.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023

Identifying Disease-related Brain Imaging Quantitative Traits and Related Genetic Variations via A Bidirectional Association Learning Method.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023

2022
Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method.
BMC Bioinform., March, 2022

A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021
Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis.
Medical Image Anal., 2021

Corrigendum to Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis [Medical Image Analysis 70 (2021) 1-12/102003].
Medical Image Anal., 2021

Improved Multi-task SCCA for Brain Imaging Genetics via Joint Consideration of the Diagnosis, Parameter Decomposition and Network Constraints.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2020
Mining High-order Multimodal Brain Image Associations via Sparse Tensor Canonical Correlation Analysis.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020


  Loading...