no code implementations • 19 May 2023 • Thomaz F. S. Bastiaanssen, Thomas P. Quinn, Amy Loughman
In this companion piece to Bugs as Features (part I), we present techniques from adjacent and disparate fields to enrich and inform the analysis of microbiome-gut-brain-axis data.
1 code implementation • 18 May 2023 • Thomaz F. S. Bastiaanssen, Thomas P. Quinn, John F. Cryan
We further extend our framework beyond pairwise association testing to differential association testing, and show how anansi can be used to identify associations that differ in strength or degree based on sample covariates such as case/control status.
no code implementations • 25 Jul 2022 • 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.
1 code implementation • 20 May 2022 • Elliott Gordon-Rodriguez, Thomas P. Quinn, John P. Cunningham
Our work extends the success of data augmentation to compositional data, i. e., simplex-valued data, which is of particular interest in the context of the human microbiome.
no code implementations • 8 Jul 2021 • Thomas P. Quinn
Failures in model verification, including test set omission and test set leakage, make it impossible to know whether or not a trained model is fit for purpose.
no code implementations • 10 Dec 2020 • Thomas P. Quinn, Stephan Jacobs, Manisha Senadeera, Vuong Le, Simon Coghlan
Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in \textit{A Christmas Carol}, who guide Ebenezer through the past, present, and future of Christmas holiday events.
no code implementations • 18 Aug 2020 • Thomas P. Quinn, Manisha Senadeera, Stephan Jacobs, Simon Coghlan, Vuong Le
These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions.
1 code implementation • 2 Jun 2020 • Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh
We define personalized interpretability as a measure of sample-specific feature attribution, and view it as a minimum requirement for a precision health model to justify its conclusions.