Results 171 to 180 of about 493,090 (289)

CellPhenoX: An Explainable Machine Learning Method for Identifying Cell Phenotypes To Predict Clinical Outcomes from Single‐Cell Multi‐Omics

open access: yesAdvanced Science, EarlyView.
CellPhenoX is an explainable machine learning framework that identifies cell‐specific phenotypes and interaction effects from single‐cell omics data. By leveraging interpretable models, it enables robust discovery of cell‐level phenotypes that contribute to clinical outcomes.
Jade Young   +4 more
wiley   +1 more source

A Replicable and Generalizable Neuroimaging‐Based Indicator of Pain Sensitivity Across Individuals

open access: yesAdvanced Science, EarlyView.
Humans differ in their sensitivity to pain. With six large and diverse fMRI datasets (total N = 1046), this study finds that such individual differences in pain sensitivity can be tracked by fMRI responses to painful stimuli. A highly generalizable machine learning model is further built to predict pain sensitivity across all datasets and analgesic ...
Li‐Bo Zhang   +7 more
wiley   +1 more source

Single‐Cell Transcriptomics Reveals ITGA2‐Mediated Metabolic Reprogramming and Immune Crosstalk in Pediatric Thyroid Carcinogenesis

open access: yesAdvanced Science, EarlyView.
This study unveils ITGA2⁺ tumor cells as key drivers of pediatric thyroid cancer aggressiveness. These cells orchestrate dual oncogenic pathways: GLUT1‐mediated glycolytic reprogramming and M2 macrophage polarization. This metabolic‐immunological crosstalk promotes tumor progression and metastasis.
Zhi‐jun Zhan   +11 more
wiley   +1 more source

Update Disturbance‐Resilient Analog ReRAM Crossbar Arrays for In‐Memory Deep Learning Accelerators

open access: yesAdvanced Science, EarlyView.
Conductive metal oxide/HfOx analog ReRAM on 350 nm technology is presented for in‐memory deep learning accelerators. The device exhibits analog and nonvolatile conductance switching and high resilience to update disturbances, which is supported by COMSOL Multiphysics simulations.
Wooseok Choi   +16 more
wiley   +1 more source

Spatiotemporal Characterization of the Functional MRI Latency Structure with Respect to Neural Signaling and Brain Hierarchy

open access: yesAdvanced Science, EarlyView.
Resting‐state fMRI captures intrinsic brain activity, yet the physical significance of latency structures remains unclear. In this study, the spatiotemporal properties of fMRI‐derived latency structures are examined by linking them to biophysical model‐based neural functions, intrinsic neural timescales, and functional gradients.
Hyoungshin Choi   +5 more
wiley   +1 more source

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