publications

2025

1. Predicting T Cell Receptor Specificity with Graph Attention Networks

   Aiwu Xu, Dhritiben Patel, Wenjun Lin, and Ping Luo

   In 12th International Work-Conference on Bioinformatics and Biomedical Engineering, 2025
2. Creation of Visualizations with a Multi-Agent LLM Approach

   Ping Luo, Kyle Gauthier, Bo Huang, and Wenjun Lin

   In 10th International Congress on Information and Communication Technology, 2025

2024

1. Early cancer derection in Li-Fraumeni Syndrome with cell-free DNA

   Derek Wong*, Ping Luo*, Leslie Oldfield, Haifan Gong, Ledia Brunga, and  others

   Cancer Discovery, 2024

2023

1. Immunophenotypic correlates of sustained MRD negativity in patients with multiple myeloma

   David G Coffey, Francesco Maura, Edgar Gonzalez-Kozlova, J Javier Diaz-Mejia, Ping Luo, Yong Zhang, Yuexin Xu, Edus H Warren, Travis Dawson, Brian Lee, and  others

   Nature communications, 2023
2. Evaluation of single-cell RNAseq labelling algorithms using cancer datasets

   Erik Christensen*, Ping Luo*, Andrei Turinsky, Mia Husić, Alaina Mahalanabis, Alaine Naidas, Juan Javier Diaz-Mejia, Michael Brudno, Trevor Pugh, Arun Ramani, and  others

   Briefings in Bioinformatics, 2023
3. Integrated, longitudinal analysis of cell-free DNA in uveal melanoma

   Derek Wong, Ping Luo, Nadia Znassi, Diana P Arteaga, Diana Gray, Arnavaz Danesh, Ming Han, Eric Y Zhao, Stephanie Pedersen, Stephenie Prokopec, and  others

   Cancer Research Communications, 2023

2022

1. Integrated analysis of cell-free DNA for the early detection of cancer in people with Li-Fraumeni Syndrome

   Derek Wong*, Ping Luo*, Leslie Oldfield, Haifan Gong, Ledia Brunga, Ron Rabinowicz, Vallijah Subasri, Clarissa Chan, Tiana Downs, Kirsten M Farncombe, and  others

   medRxiv, 2022
2. Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets

   Alaina Mahalanabis, Andrei L Turinsky, Mia Husić, Erik Christensen, Ping Luo, Alaine Naidas, Michael Brudno, Trevor Pugh, Arun K Ramani, and Parisa Shooshtari

   Computational and Structural Biotechnology Journal, 2022

2021

1. Predicting disease-associated genes: Computational methods, databases, and evaluations

   Ping Luo, Bolin Chen, Bo Liao, and Fang-Xiang Wu

   Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2021
2. Identifying cell types from single-cell data based on similarities and dissimilarities between cells

   Yuanyuan Li, Ping Luo, Yi Lu, and Fang-Xiang Wu

   BMC bioinformatics, 2021

2020

1. Ensemble disease gene prediction by clinical sample-based networks

   Ping Luo, Li-Ping Tian, Bolin Chen, Qianghua Xiao, and Fang-Xiang Wu

   BMC bioinformatics, 2020
2. Crescent: cancer single cell expression toolkit

   Suluxan Mohanraj, J Javier Dı́az-Mejı́a, Martin D Pham, Hillary Elrick, Mia Husić, Shaikh Rashid, Ping Luo, Prabnur Bal, Kevin Lu, Samarth Patel, and  others

   Nucleic Acids Research, 2020

2019

1. deepDriver: predicting cancer driver genes based on somatic mutations using deep convolutional neural networks

   Ping Luo, Yulian Ding, Xiujuan Lei, and Fang-Xiang Wu

   Frontiers in genetics, 2019
2. A novel core-attachment–based method to identify dynamic protein complexes based on gene expression profiles and PPI networks

   Qianghua Xiao, Ping Luo, Min Li, Jianxin Wang, and Fang-Xiang Wu

   Proteomics, 2019
3. Enhancing the prediction of disease–gene associations with multimodal deep learning

   Ping Luo, Yuanyuan Li, Li-Ping Tian, and Fang-Xiang Wu

   Bioinformatics, 2019
4. Identifying disease-gene associations with graph-regularized manifold learning

   Ping Luo, Qianghua Xiao, Pi-Jing Wei, Bo Liao, and Fang-Xiang Wu

   Frontiers in genetics, 2019
5. Improved spectral clustering method for identifying cell types from single-cell data

   Yuanyuan Li, Ping Luo, Yi Lu, and Fang-Xiang Wu

   In Intelligent Computing Theories and Application: 15th International Conference, ICIC 2019, Nanchang, China, August 3–6, 2019, Proceedings, Part II 15, 2019
6. Identifying disease-associated genes based on artificial intelligence

   Ping Luo, and  others

   University of Saskatchewan, 2019

2018

1. CASNMF: a converged algorithm for symmetrical nonnegative matrix factorization

   Li-Ping Tian*, Ping Luo*, Haiying Wang, Huiru Zheng, and Fang-Xiang Wu

   Neurocomputing, 2018
2. Predicting gene-disease associations with manifold learning

   Ping Luo, Li-Ping Tian, Bolin Chen, Qianghua Xiao, and Fang-Xiang Wu

   In Bioinformatics Research and Applications: 14th International Symposium, ISBRA 2018, Beijing, China, June 8-11, 2018, Proceedings 14, 2018
3. Predicting disease genes from clinical single sample-based PPI networks

   Ping Luo, Li-Ping Tian, Bolin Chen, Qianghua Xiao, and Fang-Xiang Wu

   In Bioinformatics and Biomedical Engineering: 6th International Work-Conference, IWBBIO 2018, Granada, Spain, April 25–27, 2018, Proceedings, Part I 6, 2018

2017

1. Disease gene prediction by integrating PPI networks, clinical RNA-seq data and OMIM data

   Ping Luo, Li-Ping Tian, Jishou Ruan, and Fang-Xiang Wu

   IEEE/ACM transactions on computational biology and bioinformatics, 2017