Pain Management

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?

Graph Neural Networks (GNNs) are widely adopted for fault diagnosis in microservice systems, premised on their ability to model service dependencies. However, the necessity of explicit graph structures remains underexamined, as existing evaluations conflate preprocessing with architectural contributions. To isolate the true value of GNNs, we propose DiagMLP, a deliberately minimal, topology-agno...

Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks

Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classification task. There is an issue with this representation since even missing one word from the dependency parsing graph may totally change the final prediction. To this end, the full adjacency matrix of the dependency graph...

Long-range Brain Graph Transformer

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which ...

CAPTAIN: A multimodal foundation model pretrained on co-assayed single-cell RNA and protein

Proteins act as the terminal effectors of cellular function, encoding the phenotypic consequences of genomic and transcriptomic programs. Although tra...

Molecular unbalances between striosome and matrix compartments characterize the pathogenesis of Huntington’s disease model mouse

The pathogenesis of Huntington’s disease is still incompletely understood, despite the remarkable advances in identifying the molecular effects of the...

In vivo Quantification of Neural Criticality and Complexity in Mouse Cortex and Striatum in a Model of Cocaine Abstinence

Self-organized criticality is a hallmark of complex dynamic systems at phase transitions. Systems that operate at or near criticality have large-scale...

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatia...

MoCETSE: A mixture-of-convolutional experts and transformer-based model for predicting Gram-negative bacterial secreted effectors

Identifying effector proteins of Gram-negative bacterial secretion systems is crucial for understanding their pathogenic mechanisms and guiding antimi...

Multivariate pattern analysis reveals resting-state EEG biomarkers in fibromyalgia

Fibromyalgia (FM) involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturban...

Non-polio enteroviruses compromise the electrophysiology of a human iPSC-derived neural network

The non-polio enteroviruses enterovirus-D68 (EV-D68) and enterovirus-A71 (EV-A71) are highly prevalent and considered pathogens of increasing health c...

Modeling Withdrawal States in Opioid-Dependent Mice with Machine Learning

Understanding opioid withdrawal behaviors in preclinical models is critical to improving therapeutic approaches for opioid use disorder (OUD). However...

Personalized real-time inference of momentary excitability from human EEG

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentia...

The Human Omnibus of Targetable Pockets

Hundreds of computational methods for predicting ligand binding pockets exist, but the problem of finding druggable pockets throughout the human prote...

A subtype of ultrasonic vocalizations during highly palatable food consumption in rats identified by machine learning–assisted classification

Identifying behavioral and physiological responses to rewarding stimuli is essential for understanding positive emotional states in animals and for in...

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

Alzheimer’s disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies—such as amyloid-β (Aβ), tau, cerebral ...

Toward Unified Biomarkers for Focal Epilepsy

Accurately localizing the epileptogenic network (EpiNet) remains a major barrier to effective epilepsy treatment, largely due to limited mechanistic u...

A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography

Analyzing temporal spike patterns in nociceptors recorded via microneurography is challenging due to the use of a single recording electrode, waveform...

Multi-Class Classification of Cannabis and Alcohol Use Disorder: Identifying Common and Substance-Specific Neural Circuits

Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and ...

Theta and gamma transcranial alternating current stimulation modulate Mandarin consonant and lexical tone perception

A theta/gamma oscillatory neural mechanism has been postulated to explain the auditory sampling of hierarchical syllable-phoneme structure with corres...

A blueprint for mutation-defined hallmark vulnerabilities across human cancers

Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer depe...