Juan Shu
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
Publications
30
Citations
490
Est. group size
~10
Recurring co-author estimate
Active years
6
Publishing since 2021
Juan Shu works at the intersection of computational biology and machine learning, developing computational tools and statistical methods for analyzing genetic, imaging, and single-cell biological data. Recent work includes cloud computing platforms for cross-trait genetic analysis, methods for integrating single-cell multi-omics datasets, graph-based anomaly detection, and multi-organ studies linking imaging and genetics to sleep and aging. The work combines deep learning approaches (autoencoders, neural networks) with applications in genomics, disease gene prediction, and biomedical data science.
Publication output began after 2020 and has grown substantially since, with a notable increase in output projected for 2026 compared to earlier years in the decade.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Analyzing Cross-Trait Genetic Architecture with the BIGA Cloud Computing Platform
Journal of the American Statistical Association · 2026
- Multiorgan_aging_code
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Multiorgan_aging_code
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Multiorgan_aging_code
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Multiorgan_aging_code
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Retrieve, Then Classify: Corpus-Grounded Automation of Clinical Value Set Authoring
arXiv (Cornell University) · 2026
- Retrieve, Then Classify: Corpus-Grounded Automation of Clinical Value Set Authoring
arXiv (Cornell University) · 2026
- Multi-organ imaging and genetics show the impact of sleep patterns on the human brain and body
Communications Medicine · 2026
- GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
2024
- Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration
Bioinformatics · 2023
- Disease gene prediction with privileged information and heteroscedastic dropout
Bioinformatics · 2021
- Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)
Advances in computational intelligence and robotics book series · 2021
- Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
bioRxiv (Cold Spring Harbor Laboratory) · 2021
- Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions
arXiv (Cornell University) · 2021
- Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
arXiv (Cornell University) · 2021
- medRxiv×8
- arXiv (Cornell University)×4
- Zenodo (CERN European Organization for Nuclear Research)×4
- Nature Communications×2
- Bioinformatics×2
- Bingxin Zhao
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
- Zirui Fan
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
- Xifeng Wang
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
- Bingxuan Li
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
- Jason H. Moore
Biochemistry, Genetics and Molecular Biology · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile