Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in...
Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently b...
BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as cli...
We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach re...
OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML.
Neural networks : the official journal of the International Neural Network Society
Mar 19, 2020
Cross-modal retrieval has recently attracted much interest along with the rapid development of multimodal data, and effectively utilizing the complementary relationship of different modal data and eliminating the heterogeneous gap as much as possible...
BACKGROUND: When many features and a small number of clinical data exist, previous studies have used a few top-ranked features from the Fisher's discriminant ratio (FDR) for feature selection. However, there are many similarities between selected fea...
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