Jie Pan
Orcid: 0000-0002-4993-298XAffiliations:
- Northwest University, Xi'an, China
- Xijing University, School of Information Engineering, Xi'an, China (former)
According to our database1,
Jie Pan
authored at least 14 papers
between 2020 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2026
CM-PHI: combining multi-hop attention graph neural network with sequence semantic analysis to predict phage-host interaction.
Expert Syst. Appl., 2026
2025
Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data.
CoRR, February, 2025
Machine learning for modelling unstructured grid data in computational physics: A review.
Inf. Fusion, 2025
2024
MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network.
J. Chem. Inf. Model., 2024
A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.
Briefings Bioinform., 2024
2023
PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network.
Briefings Bioinform., September, 2023
2022
A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.
Briefings Bioinform., 2022
A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.
Briefings Bioinform., 2022
2021
A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification.
Sci. Program., 2021
FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method.
Sci. Program., 2021
Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2021
2020
Prediction of Drug-Target Interactions by Ensemble Learning Method From Protein Sequence and Drug Fingerprint.
IEEE Access, 2020
Predicting Protein-Protein Interactions from Protein Sequence Using Locality Preserving Projections and Rotation Forest.
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform.
Proceedings of the Intelligent Computing Theories and Application, 2020