A XML-Based Approach to Integrating Heterogeneous Yeast Genome Data. - Kei-Hoi Cheung; Deyun Pan; Andrew K. Smith; Michael R. Seringhaus; Shawn M. Douglas; Mark Gerstein (METMBS, 2004)

YeastHub: a semantic web use case for integrating data in the life sciences domain. - Kei-Hoi Cheung; Kevin Y. Yip; Andrew K. Smith; Remko de Knikker; Andy Masiar; Mark Gerstein (ISMB (Supplement of Bioinformatics), 2005)

Editorial. - Dov Greenbaum; Andrew K. Smith; Mark Gerstein (Nucleic Acids Res., 2005)

Leveraging the structure of the Semantic Web to enhance information retrieval for proteomics. - Andrew K. Smith; Kei-Hoi Cheung; Michael Krauthammer; Martin H. Schultz; Mark Gerstein (Bioinform., 2007)

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics. - Andrew K. Smith; Kei-Hoi Cheung; Kevin Y. Yip; Martin H. Schultz; Mark Gerstein (BMC Bioinform., 2007)

Mechanistic agent-based damage and repair models as hypotheses for patterns of necrosis caused by drug induced liver injury. - Andrew K. Smith; Glen E. P. Ropella; Neil Kaplowitz; Murad Ookhtens; C. Anthony Hunt (SummerSim, 2014)

Virtual experiments help explain intra- and inter-strain variability of AILI-induced hepatic necrosis in mice. - Andrew K. Smith; Glen E. P. Ropella; C. Anthony Hunt; Neil Kaplowitz; Murad Ookhtens (SpringSim (ADS), 2016)

Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments. - Andrew K. Smith; Brenden K. Petersen; Glen E. P. Ropella; Ryan C. Kennedy; Neil Kaplowitz; Murad Ookhtens; C. Anthony Hunt (PLoS Comput. Biol., 2016)

Contrasting model mechanisms of alanine aminotransferase (ALT) release from damaged and necrotic hepatocytes as an example of general biomarker mechanisms. - Andrew K. Smith; Glen E. P. Ropella; Mitchell R. McGill; Preethi Krishnan; Lopamudra Dutta; Ryan C. Kennedy; Hartmut Jaeschke; C. Anthony Hunt (PLoS Comput. Biol., 2020)

TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning. - Alireza Bagheri Rajeoni; Breanna Pederson; Ali Firooz; Hamed Abdollahi; Andrew K. Smith; Daniel G. Clair; Susan M. Lessner; Homayoun Valafar (CoRR, 2023)