The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature ...
Abstract: Generating compact and robust feature representations using principal component analysis (PCA) is crucial for image retrieval tasks. However, most existing methods require PCA parameters to ...
This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...
San Francisco, Philadelphia and others are retreating from “harm reduction” strategies that have helped reduce deaths but which critics, including Trump, say have contributed to pervasive public drug ...
Take advantage of the MethodImplAttribute class in C# to inline methods and improve the execution speed of your .NET applications. The Just-In-Time (JIT) compiler is a component of the Common Language ...
The advancement of tactile sensing in robotics and prosthetics is constrained by the trade-off between spatial and temporal resolution in artificial tactile sensors. To address this limitation, we ...
Magnetic resonance imaging (MRI) is among the most commonly used imaging methods in preclinical studies as it non-invasively produces multiparametric data of tissues and organs. An animal organism’s ...
Here, we present Randomized Spatial PCA (RASP), a novel spatially aware dimensionality reduction method for spatial transcriptomics (ST) data. RASP is designed to be orders-of-magnitude faster than ...
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