PyMethylProcess - convenient high-throughput preprocessing workflow for DNA methylation data. - Joshua J. Levy; Alexander J. Titus; Lucas A. Salas; Brock C. Christensen (Bioinform., 2019)

PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation inDigital Pathology. - Joshua J. Levy; Lucas A. Salas; Brock C. Christensen; Aravindhan Sriharan; Louis J. Vaickus (PSB, 2020)

MethylNet: an automated and modular deep learning approach for DNA methylation analysis. - Joshua J. Levy; Alexander J. Titus; Curtis L. Petersen; Youdinghuan Chen; Lucas A. Salas; Brock C. Christensen (BMC Bioinform., 2020)

Graph Neural Networks Ameliorate Potential Impacts of Imprecise Large-Scale Autonomous Immunofluorescence Labeling of Immune Cells on Whole Slide Images. - Ramya Reddy; Ram Reddy; Cyril Sharma; Christopher R. Jackson; Scott Palisoul; Rachael Barney; Fred Kolling; Lucas A. Salas; Brock C. Christensen; Gabriel Brooks; Gregory J. Tsongalis; Louis J. Vaickus; et al. (GeoMedIA, 2022)

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)

A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures. - Shelby Bell-Glenn; Jeffrey A. Thompson; Lucas A. Salas; Devin C. Koestler (Frontiers Bioinform., 2022)

MethylMasteR: A Comparison and Customization of Methylation-Based Copy Number Variation Calling Software in Cancers Harboring Large Scale Chromosomal Deletions. - Michael P. Mariani; Jennifer A. Chen; Ze Zhang; Steven C. Pike; Lucas A. Salas (Frontiers Bioinform., 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)

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)

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)

Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models. - Weiyi Wu; Xinwen Xu; Chongyang Gao; Xingjian Diao; Siting Li; Lucas A. Salas; Jiang Gui (CoRR, 2025)