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Found 8 papers, 3 papers with code
Bugs as Features (Part II): A Perspective on Enriching Microbiome-Gut-Brain Axis Analyses
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.
Knowledge-based Integration of Multi-Omic Datasets with Anansi: Annotation-based Analysis of Specific Interactions
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.
Bugs as Features (Part I): Concepts and Foundations for the Compositional Data Analysis of the Microbiome-Gut-Brain Axis
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.
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
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.
Stool Studies Don't Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning
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.
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?
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.
Trust and Medical AI: The challenges we face and the expertise needed to overcome them
These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions.
DeepCoDA: personalized interpretability for compositional health data
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.
