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Article
Transparent medical image AI via an image–text foundation model grounded in medical literature
Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models...
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Article
Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical exp...
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Article
Isolating salient variations of interest in single-cell data with contrastiveVI
Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and the corresponding cells in a treatment condition, such as exposure to a drug or infection by a pa...
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Article
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine...
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Article
Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining machine learning models. However, their estimation is complex from both theoretical and computational standpoints. We disentangle this...
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Article
Open AccessPAUSE: principled feature attribution for unsupervised gene expression analysis
As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can ...
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Article
Open AccessPredictive and robust gene selection for spatial transcriptomics
A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell’s molecular state. This typically requires targeting an a priori selection of genes, ofte...
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Article
A cost-aware framework for the development of AI models for healthcare applications
Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads. However, when time or financial resources for gathering input data are limited, as in emergency and critical-care m...
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Article
Open AccessInterpretable machine learning prediction of all-cause mortality
Unlike linear models which are traditionally used to study all-cause mortality, complex machine learning models can capture non-linear interrelations and provide opportunities to identify unexplored risk facto...
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Article
Open AccessExplaining a series of models by propagating Shapley values
Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of ex...
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Article
Open AccessForecasting adverse surgical events using self-supervised transfer learning for physiological signals
Hundreds of millions of surgical procedures take place annually across the world, which generate a prevalent type of electronic health record (EHR) data comprising time series physiological signals. Here, we p...
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Article
University of Washington Nathan Shock Center: innovation to advance aging research
The University of Washington Nathan Shock Center of Excellence in the Basic Biology of Aging provides leadership and resources to support the geroscience community locally, nationally, and internationally. Ser...
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Article
Reproducibility standards for machine learning in the life sciences
To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By ...
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Article
Open AccessUnified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene...
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Article
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain d...
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Article
AI for radiographic COVID-19 detection selects shortcuts over signal
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using...
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Chapter
Explaining Models by Propagating Shapley Values of Local Components
In healthcare, making the best possible predictions with complex models (e.g., neural networks, ensembles/stacks of different models) can impact patient welfare. In order to make these complex models explainab...
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Article
From local explanations to global understanding with explainable AI for trees
Tree-based machine learning models such as random forests, decision trees and gradient boosted trees are popular nonlinear predictive models, yet comparatively little attention has been paid to explaining thei...
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Article
Production of a Monoclonal Antibody Targeting the M Protein of MERS-CoV for Detection of MERS-CoV Using a Synthetic Peptide Epitope Formulated with a CpG–DNA–Liposome Complex
The Middle East respiratory syndrome-related coronavirus (MERS-CoV) contains four major structural proteins, the spike glycoprotein, nucleocapsid phosphoprotein, membrane (M) glycoprotein and small envelope gl...
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Article
Production of Anti-c-Myc Monoclonal Antibody Inhibiting DNA Binding of c-Myc and Max Dimer by Epitope Peptide–CpG-DNA–Liposome Complex Without Carriers
The c-Myc protein is diversely involved in normal cellular function, and its deregulation has been implicated in several cancers. Therefore, c-Myc is a validated target for anti-cancer therapeutics. Here, we d...
