Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 21,211 to 21,220 of 216,348 articles

A widespread internal brain state for fentanyl withdrawal

bioRxiv
Opioid addiction is characterized by escalating drug use, driven in part by negative reinforcement from withdrawal, but the neural processes linking withdrawal to increased drug-taking remain poorly understood. Here, we use multisite local field pote... read more 

Cholesteryl Ester as a Prognostic Biomarker In IDH-wildtype Glioblastoma

bioRxiv
Current treatment of IDH-wildtype glioblastoma (GBM) relies on the first-line chemotherapy-temozolomide. Although MGMT methylation is routinely conducted to predict chemosensitivity, its efficacy is often compromised. Thus, there is an urgent need to... read more 

Neural Network Guided Calibration for Fast Virtual Twin Generation in Cardiovascular ODE Models

bioRxiv
Calibration of closed-loop lumped-parameter cardiovascular models remains a major bottleneck for scalable digital-twin generation because inverse estimation is ill-conditioned and typically requires computationally expensive iterative forward simulat... read more 

Input data when using neural networks to estimate lower-body torques from wearable sensors during gait: Is it of great influence?

bioRxiv
Recent advancements in wearable sensors and machine learning show promise for estimating lower-body joint torques outside of laboratory settings. Inertial Measurement Units combined with Convolutional Neural Networks have proven effective for this ta... read more 

TopoFuseNet: Hierarchical Graph Representation Learning with Multi-Scale Topological Features for Accurate Drug Synergy Prediction

bioRxiv
Accurate prediction of drug synergy is paramount for developing effective combination therapies and advancing personalized medicine. Although methods based on graph neural networks (GNNs) have become a prevalent approach, they often treat molecules a... read more 

BRIDGE: A Multi-organ Histo-ST Foundation Model Enables Virtual Spatial Transcriptomics for Enhanced Few-shot Cancer Diagnosis

bioRxiv
Recent studies have explored generating virtual spatial transcriptomics (ST) profiles from histological images, offering a promising alternative to laboratory-measured molecular profiling. However, existing approaches predominantly rely on single-org... read more 

STARMAP: A 3D-informed framework for mapping functional regions in proteins to regulatory and cellular phenotypes

bioRxiv
Artificial Intelligence (AI) has transformed biology by revealing patterns in large-scale datasets and predicting regulatory relationships. Yet even the most advanced models often fail to identify biologically meaningful mechanisms from statistical a... read more 

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

bioRxiv
Precision oncology lacks scalable tools to assess, at the patient level, systems-level tumor microenvironment (TME) programs driving therapeutic resistance. To address this gap, we trained a weakly-supervised deep learning model that uses routine H&E... read more 

A brain-inspired framework for memory prioritization in neural networks based on valence

bioRxiv
Improving long-term memory in artificial neural networks remains an open challenge. To address this, we developed a novel brain-inspired framework for memory prioritization based on the principle of emotional valence. Our framework includes: (i) a va... read more 

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

bioRxiv
Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerizati... read more