Development of biologically interpretable multimodal deep learning model for cancer prognosis prediction. - Zarif L. Azher; Louis J. Vaickus; Lucas A. Salas; Brock C. Christensen; Joshua J. Levy (SAC, 2022)

Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication. - Zarif L. Azher; Anish Suvarna; Ji-Qing Chen; Ze Zhang; Brock C. Christensen; Lucas A. Salas; Louis J. Vaickus; Joshua J. Levy (BioData Min., 2023)

Mapping Three-Dimensional Tumor Heterogeneity through Deep Learning Inference of Spatial Transcriptomics from Routine Histopathology: A Proof-of-Concept Comparative Study. - Zarif L. Azher; Gokul Srinivasan; Keluo Yao; Minh-Khang Le; Ken Lau; Harsimran Kaur; Fred Kolling; Louis J. Vaickus; Xiaoying Lu; Joshua J. Levy (ML4H@NeurIPS, 2024)

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology. - Michael Y. Fatemi; Yunrui Lu; Alos B. Diallo; Gokul Srinivasan; Zarif L. Azher; Brock C. Christensen; Lucas A. Salas; Gregory J. Tsongalis; Scott Palisoul; Laurent Perreard; Frederick W. Kolling IV; Louis J. Vaickus; et al. (Briefings Bioinform., 2024)

Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship. - Zachary R. Baker; Zarif L. Azher (CoRR, 2024)

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity. - Michael Y. Fatemi; Yunrui Lu; Zarif L. Azher; Cyril Sharma; Eric Feng; Alos B. Diallo; Gokul Srinivasan; Grace M. Rosner; Kelli B. Pointer; Brock C. Christensen; Lucas A. Salas; Gregory J. Tsongalis; et al. (BIOSTEC (1), 2025)