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Interpretable and explainable machine learning: A methods‐centric overview with concrete examples
Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …
applications in medicine, economics, law, and natural sciences and form an essential …
Interpretability and explainability: A machine learning zoo mini-tour
In this review, we examine the problem of designing interpretable and explainable machine
learning models. Interpretability and explainability lie at the core of many machine learning …
learning models. Interpretability and explainability lie at the core of many machine learning …
Beyond normal: On the evaluation of mutual information estimators
Mutual information is a general statistical dependency measure which has found
applications in representation learning, causality, domain generalization and computational …
applications in representation learning, causality, domain generalization and computational …
Compositional data analysis of the microbiome: fundamentals, tools, and challenges
Purpose Human microbiome studies are within the realm of compositional data with the
absolute abundances of microbes not recoverable from sequence data alone. In …
absolute abundances of microbes not recoverable from sequence data alone. In …
Using control genes to correct for unwanted variation in microarray data
Microarray expression studies suffer from the problem of batch effects and other unwanted
variation. Many methods have been proposed to adjust microarray data to mitigate the …
variation. Many methods have been proposed to adjust microarray data to mitigate the …
Sparse Bayesian infinite factor models
We focus on sparse modelling of high-dimensional covariance matrices using Bayesian
latent factor models. We propose a multiplicative gamma process shrinkage prior on the …
latent factor models. We propose a multiplicative gamma process shrinkage prior on the …
High-dimensional sparse factor modeling: applications in gene expression genomics
We describe studies in molecular profiling and biological pathway analysis that use sparse
latent factor and regression models for microarray gene expression data. We discuss breast …
latent factor and regression models for microarray gene expression data. We discuss breast …
Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans
Acute respiratory infections (ARIs) are a common reason for seeking medical attention, and
the threat of pandemic influenza will likely add to these numbers. Using human viral …
the threat of pandemic influenza will likely add to these numbers. Using human viral …
Bayesian exploratory factor analysis
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that
improves on ad hoc classical approaches. Our framework relies on dedicated factor models …
improves on ad hoc classical approaches. Our framework relies on dedicated factor models …
A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2
There is great potential for host-based gene expression analysis to impact the early
diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted …
diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted …