52 citations · 140 across the 9 of their papers we have counts for
9 papers
Knowledge-based Integration of Multi-Omic Datasets with Anansi: Annotation-based Analysis of Specific Interactions
Thomaz F. S. Bastiaanssen, Thomas P. Quinn, John F. Cryan
Motivation: Studies including more than one type of 'omics data sets are becoming more prevalent. Integrating these data sets can be a way to solidify findings and even to make new…
Bugs as Features (Part II): A Perspective on Enriching Microbiome-Gut-Brain Axis Analyses
Thomaz F. S. Bastiaanssen, Thomas P. Quinn, Amy Loughman
The microbiome-gut-brain-axis field is multidisciplinary, benefiting from the expertise of microbiology, ecology, psychiatry, computational biology, and epidemiology amongst other…
Bugs as Features (Part I): Concepts and Foundations for the Compositional Data Analysis of the Microbiome-Gut-Brain Axis
Thomaz F. S. Bastiaanssen, Thomas P. Quinn, Amy Loughman
There has been a growing acknowledgement of the involvement of the gut microbiome - the collection of microbes that reside in our gut - in regulating our mood and behaviour. This p…
Aitchison's Compositional Data Analysis 40 Years On: A Reappraisal
Michael Greenacre, Eric Grunsky, John Bacon-Shone +2
The development of John Aitchison's approach to compositional data analysis is followed since his paper read to the Royal Statistical Society in 1982. Aitchison's logratio approach…
Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education
Thomas P Quinn, Simon Coghlan
Medical students will almost inevitably encounter powerful medical AI systems early in their careers. Yet, contemporary medical education does not adequately equip students with th…
Stool Studies Don't Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning
Thomas P. Quinn
In the machine learning culture, an independent test set is required for proper model verification. Failures in model verification, including test set omission and test set leakage…
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction
Tri Minh Nguyen, Thomas P Quinn, Thin Nguyen +1
Motivation: Many high-performance DTA models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more t…
Trust and Medical AI: The challenges we face and the expertise needed to overcome them
Thomas P. Quinn, Manisha Senadeera, Stephan Jacobs +2
Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. However, failures of medical AI could have serious consequences for both clinical outcomes…
DeepCoDA: personalized interpretability for compositional health data
Thomas P. Quinn, Dang Nguyen, Santu Rana +2
Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust. In the healthcare setting,…