exprso: an R-package for the rapid implementation of machine learning algorithms. - Thomas P. Quinn; Daniel Tylee; Stephen J. Glatt (F1000Research, 2016)

Understanding sequencing data as compositions: an outlook and review. - Thomas P. Quinn; Ionas Erb; Mark F. Richardson; Tamsyn M. Crowley (Bioinform., 2018)

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods. - Thomas P. Quinn; Tamsyn M. Crowley; Mark F. Richardson (BMC Bioinform., 2018)

Visualizing balances of compositional data: A new alternative to balance dendrograms. - Thomas P. Quinn (F1000Research, 2018)

Improving the classification of neuropsychiatric conditions using gene ontology terms as features: Figure Data. - Thomas P. Quinn; Samuel C. Lee; Svetha Venkatesh; Thin Nguyen (Zenodo, 2018)

Improving the classification of neuropsychiatric conditions using gene ontology terms as features: Figure Data. - Thomas P. Quinn; Samuel C. Lee; Svetha Venkatesh; Thin Nguyen (Zenodo, 2018)

DeepCoDA: personalized interpretability for compositional health data. - Thomas P. Quinn; Dang Nguyen 002; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (ICML, 2020)

Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient. - Haripriya Harikumar; Thomas P. Quinn; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (BioData Min., 2021)

Learning sparse log-ratios for high-throughput sequencing data. - Elliott Gordon-Rodríguez; Thomas P. Quinn; John P. Cunningham (Bioinform., 2021)

GraphDTA: predicting drug-target binding affinity with graph neural networks. - Thin Nguyen; Hang Le 003; Thomas P. Quinn; Tri Nguyen; Thuc Duy Le; Svetha Venkatesh (Bioinform., 2021)

Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction. - Tri Minh Nguyen 005; Thomas P. Quinn; Thin Nguyen; Truyen Tran 001 (CoRR, 2021)

Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education. - Thomas P. Quinn; Simon Coghlan (CoRR, 2021)

A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research. - Thomas P. Quinn; Sunil Gupta 001; Svetha Venkatesh; Vuong Le (CoRR, 2021)

Trust and medical AI: the challenges we face and the expertise needed to overcome them. - Thomas P. Quinn; Manisha Senadeera; Stephan Jacobs; Simon Coghlan; Vuong Le (J. Am. Medical Informatics Assoc., 2021)

PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse. - Thin Nguyen; Samuel C. Lee; Thomas P. Quinn; Buu Minh Thanh Truong; Xiaomei Li; Truyen Tran 001; Svetha Venkatesh; Thuc Duy Le (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. - Elliott Gordon-Rodríguez; Thomas P. Quinn; John P. Cunningham (NeurIPS, 2022)

The three ghosts of medical AI: Can the black-box present deliver? - Thomas P. Quinn; Stephan Jacobs; Manisha Senadeera; Vuong Le; Simon Coghlan (Artif. Intell. Medicine, 2022)

Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples. - Tri Minh Nguyen 005; Thomas P. Quinn; Thin Nguyen; Truyen Tran 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Ethics of using artificial intelligence (AI) in veterinary medicine. - Simon Coghlan; Thomas P. Quinn (AI Soc., 2024)