High-throughput and efficient multilocus genome-wide association study on longitudinal outcomes. - Huang Xu 005; Xiang Li; Yaning Yang; Yi Li; Jose Pinheiro; Kate Sasser; Hisham Hamadeh; Xu Steven 001; Min Yuan (Bioinform., 2020)

Adaptive weighted sum tests via LASSO method in multi-locus family-based association analysis. - Rui Liu; Min Yuan; Huang Xu 005; Pinzhong Chen; Xu Steven Xu; Yaning Yang (Comput. Biol. Chem., 2020)

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling. - Min Yuan; Xu Steven Xu; Yaning Yang; Yinsheng Zhou; Yi Li; Jinfeng Xu 001; Jose Pinheiro (Briefings Bioinform., 2021)

A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer. - Xingyu Li; Jitendra Jonnagaddala; Shuhua Yang; Hong Zhang 037; Xu Steven Xu (CoRR, 2021)

Bias correction for multiple covariate analysis using empirical bayesian estimation in mixed-effects models for longitudinal data. - Yi Li; Yaning Yang; Xu Steven Xu; Min Yuan; Alzheimer's Disease Neuroimaging Initiative (Comput. Biol. Chem., 2022)

Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model. - Xingyu Li; Min Cen; Jinfeng Xu 001; Hong Zhang 037; Xu Steven Xu (CoRR, 2022)

Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering. - Zihan Chen 002; Xingyu Li; Miaomiao Yang; Hong Zhang 037; Xu Steven Xu (CoRR, 2022)

Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer. - Bangwei Guo; Jitendra Jonnagaddala; Hong Zhang 037; Xu Steven Xu (CoRR, 2022)

Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning. - Xingyu Li; Jitendra Jonnagaddala; Min Cen; Hong Zhang 037; Xu Steven 001 (Entropy, 2022)

Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines. - Min Cen; Xingyu Li; Bangwei Guo; Jitendra Jonnagaddala; Hong Zhang 037; Xu Steven Xu (CoRR, 2023)

DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture. - Min Cen; Xingyu Li; Bangwei Guo; Jitendra Jonnagaddala; Hong Zhang 037; Xu Steven Xu (CoRR, 2023)

CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network. - Minghua Zhao; Min Yuan; Yaning Yang; Steven X. Xu 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations. - Bangwei Guo; Xingyu Li; Miaomiao Yang; Hong Zhang 037; Xu Steven Xu (Comput. Medical Imaging Graph., 2023)

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response. - Minghua Zhao; Steven X. Xu 001; Yaning Yang; Min Yuan (J. Chem. Inf. Model., 2023)

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping. - Fengrong Liu; Yaning Yang; Xu Steven Xu; Min Yuan (Comput. Biol. Chem., 2024)

SuperPromptSeg: A Novel Fine-Tuning-Free Segmentation Method Leveraging Superpixel-Based Point Prompts. - Ziqiao Zhou; Min Cen; Yuzhe Zhang; Hong Zhang 037; Xu Steven Xu (ICASSP, 2025)

Discovery of mutations predictive of survival benefit from immunotherapy in first-line NSCLC: A retrospective machine learning study of IMpower150 liquid biopsy data. - Min Yuan; Wei Feng; Haolun Ding; Yaning Yang; Xu Steven Xu (Comput. Biol. Medicine, 2025)