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• A comprehensive survey on graph neural networks for gene regulatory network inference.
  The gene regulatory network (GRN) represents a complex web of genetic interactions that governs cellular functions and responses to environmental stimuli. Understanding these intricate relationships is crucial for advancing developmental biology, disease model…
  europepmc 2026 置信度 0.80
• A Spatially-Aware Multimodal Deep Learning Framework with Graph Neural Network-Concept Bottleneck Fusion for Interpretable Wound Classification
  Chronic and acute wounds are a significant and increasing burden on health care systems worldwide. A key step in ensuring timely and evidence-based treatment planning and intervention is accurate, automated wound-type classification. Previous deep learning-bas…
  europepmc 2026 置信度 0.80
• Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability.
  Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approaches still face challenges with generalizability, noise robustness, and interpretability. In this study, we propo…
  europepmc 2026 置信度 0.80
• Integrating Neuromorphic Computing and Clinical Biomarkers for Enhanced Liver Disease Detection
  Abstract Liver diseases should be detected at an early stage and accurately to avoid the development of cirrhosis, liver failure, and permanent hepatocellular damage. Nevertheless, the current diagnostic models are based on computationally intensive machine le…
  europepmc 2026 置信度 0.80
• Defective wheat kernel classification using dual-range hyperspectral imaging and an interpretable spectral-spatial fusion convolutional neural network.
  The rapid and non-destructive screening of defective wheat kernels is essential for quality assurance and process control, yet reliable identification remains challenging due to subtle spectral and spatial differences between defective and sound wheat kernels.…
  europepmc 2026 置信度 0.80
• Analysis of the interaction network relationship between drugs using a graph neural network.
  Introduction The ever-increasing complexity of biochemical systems, alongside the rapid growth of pharmaceutical and biomedical data, underscores the urgent need for intelligent, scalable, and interpretable computational models. These models must be capable of…
  europepmc 2026 置信度 0.80
• Repair Instead of Retraining: A Constraint-Guided Framework for Neural Network Repair
  Abstract Deep neural networks remain vulnerable to adversarial perturbations and backdoor attacks, yet repairing a deployed model without full retraining must balance interpretability, behavioral fidelity, and computational cost. We present a constraint-guided…
  europepmc 2026 置信度 0.80
• KAN-based symbolic QSPR modeling for molecular property prediction.
  Molecular property prediction faces a persistent trade-off between predictive accuracy and model interpretability. Graph neural networks achieve high accuracy through end-to-end learning from molecular graphs, but their predictions are difficult to interpret. …
  europepmc 2026 置信度 0.80
• A Hybrid CNN–SPN Framework for Reliable Monitoring and Instability Detection in Hadoop Clusters
  Abstract Reliable monitoring of Hadoop clusters requires models that can capture both the structural dynamics of distributed node behavior and the stochastic variability of workloads, failures, and recovery processes. Existing approaches based solely on rule-b…
  europepmc 2026 置信度 0.80
• Computational hybrid approach of PBPK simulation and CGCNN models for human intravenous pharmacokinetics.
  In drug discovery, it is not sufficient for a lead compound to exhibit high binding affinity for the target protein alone. It is equally important to predict an optimal pharmacokinetic (PK) profile that reflects the physicochemical properties of the compound. …
  europepmc 2026 置信度 0.80
• D3I-COMCF: Dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments for drug-drug interaction prediction.
  Drug-drug interaction (DDI) prediction seeks to identify pharmacodynamic and pharmacokinetic interactions arising from the co-administration of multiple drugs with differing physicochemical properties. However, most existing methods predominantly focus on glob…
  europepmc 2026 置信度 0.80
• Architecture-data matching for EEG-EMG decoding: compact deep models match classical spectral decoders on the WAY-EEG-GAL grasp-and-lift dataset.
  Modern deep learning has broadened the tools available for non-invasive neural decoding, but its advantage over well-engineered classical pipelines remains unclear at clinical neural-engineering sample sizes. We compared four classical decoders, three multilay…
  europepmc 2026 置信度 0.80
• iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.
  Precise resolution of cellular heterogeneity within complex tissues is fundamental to deciphering disease etiologies from bulk transcriptomic profiles. While computational deconvolution offers a scalable alternative, current deep learning methods predominantly…
  europepmc 2026 置信度 0.80
• Unveiling subject-specific causal latency in motor imagery: a physiologically transparent BCI via Riemannian tangent space fusion.
  Objective. While deep learning (DL) has improved motor imagery (MI) brain-computer interfaces (BCIs), its 'black-box' nature lacks physiological interpretability. Building upon our previous findings that cortical state transitions are governed by non-linear ne…
  europepmc 2026 置信度 0.80
• Explainable GeoAI Framework for Groundwater Pollution Prediction, Vulnerability Mapping, and Sustainable Water Intelligence in Semi-Arid Regions
  Abstract Groundwater contamination in semi-arid regions is increasingly influenced by complex hydrogeochemical interactions and anthropogenic activities, necessitating adaptive and interpretable environmental intelligence frameworks for sustainable groundwater…
  europepmc 2026 置信度 0.80
• CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.
  Background Genomic selection serves as an effective approach to accelerate the improvement of agronomic traits in crops. However, as a core technique in modern crop breeding, genomic selection still faces many challenges in capturing complex interactions among…
  europepmc 2026 置信度 0.80
• ConMGIN: Interpretable multilayer GIN-Bayesian framework for spatial domain analysis.
  Accurate identification of spatial domains is crucial for processing spatial transcriptomics in the field of bioinformatics engineering. However, existing methods primarily focus on the similarity of spatial information while neglecting differences in gene exp…
  europepmc 2026 置信度 0.80
• Hyperspectral imaging-based dual-task learnable spectral mask convolutional neural network for hierarchical adulteration detection in Fritillaria (Beimu) powder.
  Reliable, non-destructive, and interpretable methods for detecting hierarchical adulteration in powdered herbal medicines are essential for stringent quality control. This study introduces a dual-attention Learnable Spectral Mask Convolutional Neural Network (…
  europepmc 2026 置信度 0.80
• Multimodal graph learning for chagas disease classification.
  The accurate classification of Chagas disease stages based on Trypanosoma cruzi infection in experimental models faces challenges due to the scarcity of data and the high dimensionality of diagnostic sources. This study proposes an architecture based on Graph …
  europepmc 2026 置信度 0.80
• A Physics-Informed Neural Network Framework Integrating Soft and Hard Constraints for Predicting Biomass Gasification Syngas Compositions.
  Machine learning methods have demonstrated promising applications in biomass gasification modeling. However, conventional machine learning models primarily rely on experimental data and do not account for the reaction mechanisms of gasification. When data samp…
  europepmc 2026 置信度 0.80
• MiracleNet: A Biologically Interpretable Machine Learning Model for Resected Non-small-cell Lung Cancer.
  Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Accurate prediction of relapse is notoriously difficult, posing substantial challenges to patient care and necessitating advanced tools to improve prognostic outcomes. MicroRN…
  europepmc 2026 置信度 0.80
• Digital pathology of the living brain: a voxel-level spatio-temporal network for explainable ADHD diagnosis from raw rs-fMRI across multiple scanner sites.
  Introduction Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders, affecting approximately 5-7% of children and adolescents worldwide. Clinical diagnosis currently relies on behavioral assessments that are s…
  europepmc 2026 置信度 0.80
• A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein-Protein Interaction Detection.
  The exponential growth of genomic and proteomic data has made computational protein-protein interaction (PPI) prediction indispensable, driving the need for a comprehensive and method-aware evaluation of supervised learning approaches. PPIs are fundamental to …
  europepmc 2026 置信度 0.80
• GraESM-FuseDTA: adaptive gated multimodal fusion of graph neural networks and protein language models for robust drug-target affinity prediction.
  Accurate drug-target affinity prediction is essential for virtual screening, lead compound prioritization, and drug repositioning, especially under cold-start scenarios involving unseen compounds, unseen targets, or unseen drug-target combinations. However, ex…
  europepmc 2026 置信度 0.80
• Automated detection of freeze-thaw signatures in archaeological sediments using deep learning
  Background: Freeze-thaw processes leave diagnostic traces in archaeological soils and sediments that are central to reconstructing past climates and understanding hominin adaptations to glacial environments. Frost features can be defined through soil micromorp…
  europepmc 2026 置信度 0.80
• CA-GCN: A Causality-Aware Graph Coupling Network for Multimodal Learning of Brain Connectivity.
  Understanding the coupling relationship between structural connectivity (SC) and functional connectivity (FC) is essential for advancing our knowledge of individual brain function and organization. Recent deep learning techniques, particularly graph neural net…
  europepmc 2026 置信度 0.80
• HERO-SNN: A Homeostatic Eligibility-based Reward- Optimised Spiking Neural Network for Spatiotemporal Brain Representation: An EEG Case Study
  Abstract We propose a new framework for Homeostatic Eligibility-based Reward-Optimised Spiking Neural Network (HERO-SNN) that enables spiking neural networks (SNNs) to achieve an optimal refinement in internal spatiotemporal representations based on task perfo…
  europepmc 2026 置信度 0.80
• A Hybrid Physics-Augmented Neural Network for Dynamic System Modeling with Partially Known Dynamics
  This paper introduces Hybrid Physics-Augmented Neural Network (HyPA-Net), a hybrid modeling framework that integrates physics-based linear time-invariant models with artificial neural networks (ANNs) to address dynamic system modeling when only partial physica…
  europepmc 2026 置信度 0.80
• From features to functional maps: An attention based framework for explainable memory-related brain network analysis.
  Memory encoding, during naturalistic narrative-experiences, engages distributed brain networks that extend beyond commonly reported brain regions of interest (ROIs). Although, several recent studies have reported the role of white-matter (WM) and cerebellum in…
  europepmc 2026 置信度 0.80
• SparsePool: A Graph Pooling Framework via Sparse Representation for Graph Classification.
  Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effective…
  europepmc 2026 置信度 0.80
• A Review of Physics-Informed and Data-Driven Surrogate Models for Power Transformer Fault Diagnosis.
  Power transformers are critical components of modern power grids, and their operational reliability directly affects power system security, stability, and continuity. With the increasing intelligence and complexity of power systems, condition monitoring and fa…
  europepmc 2026 置信度 0.80
• TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.
  Integration and analysis of multi-omics data provide valuable insights for improving cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Graph neural netw…
  europepmc 2026 置信度 0.80
• Causal Graph Spatial--Temporal Autoencoder for Reliable and Interpretable Process Monitoring.
  To improve the reliability and interpretability of industrial process monitoring, this article proposes a causal graph spatial-temporal autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a correlation graph structure learning mod…
  europepmc 2026 置信度 0.80
• PMB-NN: Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography.
  Background and objective Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compliance (C) are critical for early vascular dysfunction detection and therapy optimization. While photoplethysmog…
  europepmc 2026 置信度 0.80
• AI-Lagrangian MPPT: A New Paradigm for Explainable and High-Performance Photovoltaic Optimization
  The major challenge in photovoltaic (PV) maximum power point tracking (MPPT) systems is finding a balance between the high performance of artificial intelligence techniques and the interpretability and reliability of physics-based approaches. This paper propos…
  europepmc 2026 置信度 0.80
• A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.
  Background Sepsis is a major cause of postoperative morbidity and mortality, and early risk stratification from perioperative electronic health records (EHR) is a representative large-scale, high-dimensional data processing problem that requires models to be a…
  europepmc 2026 置信度 0.80
• Linguistic Efficiency and Inhibitory Control: Evaluating the DIINA Model on Afrikaans as a Morphologically Simplified but Context-Rich Language
  Abstract Afrikaans presents a compelling test case for computational models of negation and ambiguity resolution: it is morphologically relatively streamlined, yet it relies heavily on context, syntax, and discourse-level interpretation to disambiguate meaning…
  europepmc 2026 置信度 0.80
• Deep learning in food quality and safety management: A review of architectures, applications, and future directions.
  As a cornerstone of the modern food industry, process control in food processing operations undergirds food quality assurance, safety compliance and economic performance of an enterprise. With artificial intelligence technology further penetrating into the foo…
  europepmc 2026 置信度 0.80
• scLEMBAS: Context-Aware Modeling of Signaling Pathway Activity at Single-Cell Resolution
  Cells sense and integrate extracellular cues through intracellular signaling networks that reshape transcription factor activity to dictate cellular responses. Signaling activity is difficult to decipher: it is non-linear, and it contains extensive feedback an…
  europepmc 2026 置信度 0.80
• Multimodal neuroimaging fusion with hierarchical structure-function coupling for autism spectrum disorder diagnosis.
  Multimodal neuroimaging fusion has gained considerable attention in diagnosing autism spectrum disorder (ASD) due to its ability to integrate complementary information across different modalities. However, most existing approaches rely on simple feature concat…
  europepmc 2026 置信度 0.80
• Edge-aware GAT-based protein binding sites prediction.
  Accurate identification of protein binding sites is crucial for understanding biomolecular interaction mechanisms and for the rational design of drug targets. Traditional predictive methods often struggle to balance prediction accuracy with computational effic…
  europepmc 2026 置信度 0.80
• Quantum-inspired interpretable deep learning architecture for text sentiment analysis.
  Text has become the dominant medium of communication on social media, carrying rich emotional signals. Extracting such information is thus crucial for understanding public opinion and user behavior. While existing sentiment analysis approaches have achieved no…
  europepmc 2026 置信度 0.80
• Bayesian Networks and Causal Discovery.
  The discovery of the precise causal representations underlying complex data forms the bedrock of artificial intelligence research [...].
  europepmc 2026 置信度 0.80
• The Patent Landscape of AI-Based Imaging Diagnostics for Osteoporosis: A Scoping Review.
  Background Osteoporosis is a widespread skeletal disorder characterized by reduced bone density and increased fracture risk. Early detection is critical to preventing disability and healthcare burden. Methods A systematic search was conducted in Google Patents…
  europepmc 2026 置信度 0.80
• Matrix-Aware Electrochemical Sensing: A Neural Network Approach to Deciphering Medium-Specific Surface Dynamics.
  Accurate monitoring in complex biofluids remains challenging due to unpredictable skin - electrode interfacial interference and matrix-dependent impedance variations. Here, we present an intelligent enzymatic microneedle platform that achieves calibration-effi…
  europepmc 2026 置信度 0.80
• Explainable Artificial Intelligence for Thermal Condition Assessment of High-Voltage Power Equipment Using Infrared Imaging.
  Infrared thermography is widely used for non-contact thermal monitoring of high-voltage power equipment, where abnormal temperature patterns may indicate developing faults or insulation degradation. However, purely data-driven deep learning models may produce …
  europepmc 2026 置信度 0.80
• Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images.
  Neurological disorders (ND) impact a significant number of the population all over the world, affecting the brain, spinal cord, and peripheral nerves. These disorders are classified as NeuroDegenerative, NeuroBiological, and NeuroDevelopmental disorders, which…
  europepmc 2026 置信度 0.80
• Deep learning-enabled tissue engineering scaffold classification using cell morphology.
  The physical properties and architecture of biomaterial scaffolds regulate cell morphology and function; however, evaluating these effects is often slow, low-throughput, and destructive, thereby limiting scalable biomaterial design and optimization. We investi…
  europepmc 2026 置信度 0.80
• Hierarchical sparse spatiotemporal graph neural network for brain graph classification.
  Brain graph classification from resting-state fMRI (rs-fMRI) can support the identification of neurological conditions and inform personalized analysis. Here, we present a hierarchical sparse spatiotemporal graph neural network (STGNN)-GLNSTGNN-to address spar…
  europepmc 2026 置信度 0.80
• Diagnosis of Parkinson's Disease Based on Voice and Speech Using Machine Learning and Deep Learning: A Systematic Umbrella Review.
  Objectives Early diagnosis of Parkinson's disease (PD) is complicated. Speech impairment, as an early symptom of PD, offers a noninvasive, scalable biomarker for remote assessment. Speech-based machine learning has shown promise, but methodological quality of …
  europepmc 2026 置信度 0.80
• Parkinson disease severity detection based On OPtFuzNet with fused features.
  Background Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions of people worldwide. It severely impairs patients' mobility. For effective treatment strategies, it is essential to determine the severity of the disease at an e…
  europepmc 2026 置信度 0.80
• Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.
  Background Traditional genetic mapping has advanced plant trait studies but struggles to capture epistasis, pleiotropy, and genotype-environment (G × E) interactions in genomic prediction (GP). Recently, artificial intelligence (AI) has provided innovative met…
  europepmc 2026 置信度 0.80
• XHIC-Net: An Explainable Hybrid Involution-Convolution Network for Blood Smear Cell Morphology Classification.
  Accurate morphological analysis of blood smears is vital for hematological diagnosis, yet manual examination is labor-intensive and subjective. While deep learning offers automation, its black-box nature and computational demands often hinder clinical trust an…
  europepmc 2026 置信度 0.80
• DR.DEGMON: self-explainable deep neural network for drug-induced cell viability prediction incorporating differentially expressed genes and gene ontology.
  Background Accurate prediction of cancer drug responses is essential for advancing cancer treatment strategies and drug development. With the increasing availability of large-scale pharmacogenomic datasets, many deep learning models have been proposed to predi…
  europepmc 2026 置信度 0.80
• Will predictive AI make discovery research in metabolism obsolete.
  Human metabolism operates as a deeply interconnected network that maintains metabolic homoeostasis by coordinating glucose and lipid utilization, amino acid synthesis, and other core biochemical processes with the handling of exogenous compounds such as drugs …
  europepmc 2026 置信度 0.80
• Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.
  Identifying phase coupling from electrophysiological signals recorded by multiple electrodes, such as electroencephalogram (EEG) and electrocorticography (ECoG), helps neuroscientists and clinicians understand underlying brain structures or mechanisms. From a …
  europepmc 2026 置信度 0.80
• Network-Enhanced Neuro-Symbolic Framework for Financial Fraud Detection: Integrating Corporate Network Topology with Interpretable AI
  Abstract Corporate fraud imposes substantial costs on investors and the broader economy, yet existing detection methods face a fundamental trade-off between accuracy and interpretability. Traditional rule-based approaches provide transparency but achieve limit…
  europepmc 2026 置信度 0.80
• Joint optimization transposed projection envelope linear discriminant analysis mode.
  Linear Discriminant Analysis (LDA) is a widely employed feature-extraction technique that, guided by Fisher discriminant criterion, projects original high-dimensional samples into a lower-dimensional subspace with enhanced separability. Its interpretability an…
  europepmc 2026 置信度 0.80
• EEGDecoder-x: an explainable deep learning framework for cross-subject EEG-based detection of Alzheimer's and Creutzfeldt-Jakob disease.
  Early detection of neurodegenerative diseases is critical. Distinguishing early-stage Creutzfeldt-Jakob disease (CJD) from "mimics" like Alzheimer's disease (AD) remains a major challenge; while EEG is valuable in advanced CJD, early-stage abnormalities are of…
  europepmc 2026 置信度 0.80
• Deciphering tuberculosis pathway mechanisms via graph neural networks and multimodal deep learning: a comprehensive AI-driven framework for precision medicine.
  Background Tuberculosis (TB) remains a global health crisis, with complex molecular mechanisms that are not fully understood. Traditional pathway analysis methods fail to capture the intricate non-linear relationships within biological networks. Methods We dev…
  europepmc 2026 置信度 0.80
• A hybrid underwater crack image enhancement method.
  This study proposes a hybrid underwater crack image processing method. The first half of the algorithm is based on traditional image processing, which analyzes and judges the color of an image, establishes the image chromaticity factor K, and performs color co…
  europepmc 2026 置信度 0.80
• Physics-informed neural ordinary differential equations for PBPK-based internal dose prediction in environmental health risk assessment.
  Reliable prediction of internal doses, especially systemic blood concentrations, is crucial for exposure science and environmental health risk assessment. While traditional physiologically based pharmacokinetic (PBPK) models provide mechanistic insights, their…
  europepmc 2026 置信度 0.80
• Systematic validation of graph neural network explanations against adverse outcome pathway reactive centers for skin sensitization.
  Whether graph neural network (GNN) attributions capture the reactive chemistry encoded in adverse outcome pathway (AOP) annotations for skin sensitization has not been tested under a label-permutation control. We trained AttentiveFP on a 436-molecule LLNA-labe…
  europepmc 2026 置信度 0.80
• Advances in multimodal data integration for drug efficacy prediction: Methodological evolution and clinical translation.
  Drug efficacy prediction remains a cornerstone of drug development and precision therapy. However, integrating heterogeneous biomedical data, including multi-omics profiles, pathological imaging, electronic health records, and pharmacokinetic-pharmacodynamic (…
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning with Prediction Uncertainty Quantification for <i>d</i><sub>33</sub> in (K<sub>0.5</sub>Na<sub>0.5</sub>) NbO<sub>3</sub>-Based Lead-Free Piezoelectric Ceramics.
  The accelerated discovery of high-performance lead-free piezoelectric ceramics is hindered by the vast compositional space and the limited interpretability of conventional machine learning (ML) models. Here, we propose a physics-informed and interpretable ML f…
  europepmc 2026 置信度 0.80
• AI4EVER: A Graphical Deep Learning Platform For GWAS-Informed Genomic Prediction
  Deep learning (DL) has significant potential in genomic selection (GS), but its use is constrained by the technical expertise required to design and implement neural networks. Although DL has transformed fields such as language processing and structural biolog…
  europepmc 2026 置信度 0.80
• Cross-Species Multitask Learning with Molecular and ADME Descriptors for Liver Microsomal Metabolic Stability.
  Liver microsomal metabolic stability is a key determinant of in vivo exposure and an essential filter in lead optimization, yet cross-species prediction remains difficult because of heterogeneous metabolic pathways and limited model interpretability. We propos…
  europepmc 2026 置信度 0.80
• A Scoping Review of Interpretable AI Model Approaches in Echocardiography: Frameworks, Model Types, Explanation Strategies, and Evaluation Practices.
  Echocardiography is a central modality for cardiac diagnosis; rising clinical demand and advances in machine learning have accelerated AI development for automated analysis. As these systems may influence high-stakes decisions, model interpretability is essent…
  europepmc 2026 置信度 0.80
• Fetal health classification: a deep learning model with enhanced interpretability and lightweight deployment.
  Fetal health classification is crucial in the detection of potential pregnancy complications at an early level to enable timely medical intervention. Traditional diagnostic techniques rely on expert interpretation of cardiotocography (CTG) recordings, which is…
  europepmc 2026 置信度 0.80
• MUnet: A model-based unrolled reconstruction framework for computational photoacoustic imaging.
  Photoacoustic imaging provides unique optical contrast with ultrasonic penetration, enabling high-resolution visualization of biological structures in vivo. However, robust image reconstruction remains challenging under practical conditions such as limited-vie…
  europepmc 2026 置信度 0.80
• An interpretable and explainable neural network to classify sports-related cardiac arrhythmias in professional football athletes
  Sudden cardiac death risk is 2-3-fold higher in athletes than in non-athletes. We classify sports-related cardiac arrhythmias using a novel explainability framework comprising data analysis, model interpretability, post-hoc visualisation, and systematic assess…
  europepmc 2026 置信度 0.80
• Artificial intelligence (AI) as a catalyst for mechanistic target discovery: Integrating systems pharmacology and multimodal data.
  Artificial intelligence (AI) is evolving from a predictive tool into a foundational computational infrastructure for mechanism-driven pharmacology, fundamentally reshaping drug discovery. This review examines how this transformation addresses persistent challe…
  europepmc 2026 置信度 0.80
• A hybrid CNN-LSTM approach for large-scale Twitter sentiment analysis with explainable artificial intelligence.
  Sentiment analysis of social media data has become an essential research topic due to the widespread use of platforms such as Twitter for expressing public opinion. However, the short, informal, and context-dependent nature of tweets poses significant challeng…
  europepmc 2026 置信度 0.80
• Evaluating the biological meaning of neural network decisions in EEG-based MCI detection.
  Objective. High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiological understanding. This study examines whether a Transformer-based model trained on resting-state electroenceph…
  europepmc 2026 置信度 0.80
• Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.
  Background and aims Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming healthcare by enabling improved diagnosis, prognosis, and personalized treatments. However, the opacity of many AI models operates as "…
  europepmc 2026 置信度 0.80
• The Emerging Role of Explainable Artificial Intelligence in EEG-Based Autism Research: A Systematic Review.
  The increasing prevalence of Autism Spectrum Disorder (ASD) has intensified research efforts aimed at clarifying its neurobiological underpinnings. Electroencephalography (EEG) has enabled the identification of functional alterations in neuronal networks, cont…
  europepmc 2026 置信度 0.80
• Causality-Aware Spatiotemporal Adversarial Learning for Knowledge-Data Fault Diagnosis in Large-Scale Industrial Processes.
  Effective process monitoring and fault diagnosis (PMFD) are essential for safe and efficient operation in large-scale industrial processes. However, many existing methods still suffer from limited interpretability, insufficient exploitation of process knowledg…
  europepmc 2026 置信度 0.80
• Online sensor-guided hybrid ordinary differential equation-based model of nitrification dynamics in a full-scale municipal wastewater treatment plant.
  Accurate modeling of nitrification using mechanistic activated sludge models (ASMs) remains challenging because biological dynamics are typically calibrated through laborious and expert-dependent procedures. In this study, ASM No. 3 (ASM3) was integrated with …
  europepmc 2026 置信度 0.80
• Cloud EEG Privacy Using Red-Billed Blue Magpie Optimized Physics-Penalized Dual-Branch Spectral-Spatial Neural Network for Epileptic Seizure Prediction.
  Epileptic seizure prediction is a critical research area that enables timely intervention and prevention of severe neurological complications. With the growing integration of IoT in healthcare, real-time EEG monitoring has become essential for continuous and a…
  europepmc 2026 置信度 0.80
• Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.
  Background Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasingly investigated for various medical applications as a complementary or even alternative modality to traditiona…
  europepmc 2026 置信度 0.80
• Learning stochastic cellular dynamics from snapshots through bidirectional causal neural stochastic differential equations.
  Due to cell destruction in sequencing, time-series data yield unpaired snapshots, obscuring lineages and gene dynamics in individual cells. Current data-driven methods rely on single-variate expression, ignoring cell types and rotational effects in nonsteady s…
  europepmc 2026 置信度 0.80
• A Physics-Informed Dual-Branch LSTM Network for UAV Position and Attitude Estimation.
  To mitigate error accumulation and long-term drift in unmanned aerial vehicle (UAV) position and attitude estimation using purely inertial measurement unit (IMU) data, this paper presents a dual-branch physics-informed long short-term memory (DPI-LSTM) network…
  europepmc 2026 置信度 0.80
• EEG-Based Identification of Adolescent Non-Suicidal Self-Injury and Neurophysiological Interpretation Using an Explainable Deep Learning Framework
  ABSTRACT Non-suicidal self-injury (NSSI) among adolescents is a prevalent mental health problem and an important indicator of potential suicide risk. Early objective identification and neural mechanism analysis are therefore crucial for clinical screening and …
  europepmc 2026 置信度 0.80
• Stability-Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network-Based Machine Learning Approach.
  Objectives To develop a network-based machine learning framework for classifying psychosis using EEG connectivity features and to identify stable, reproducible candidate markers through a stability-driven approach. Methods This study was designed as a cross-se…
  europepmc 2026 置信度 0.80
• Development and evaluation of explainable machine learning models for predicting prognosis in patients with primary biliary cholangitis.
  Background This study aimed to develop, validate and evaluate interpretable machine learning models using clinical and laboratory data for prognosis prediction in patients with primary biliary cholangitis (PBC). Methods This study included 7905 patients treate…
  europepmc 2026 置信度 0.80
• Dynamic evolution of readmission risk factors across short-, medium-, and long-term horizons in type 2 diabetes: a machine learning-based predictive modeling study with SHAP interpretability.
  Background T2DM readmission risk factors may evolve across time windows, but this dynamic remains poorly understood. Methods This retrospective cohort study developed nine machine learning models to predict 30-day, 60-day, and 365-day readmission in 12,041 T2D…
  europepmc 2026 置信度 0.80
• Simulation-Based Explainable AI for Quantum Dynamics: Neural Proxies and SHAP for Entanglement Analysis
  Abstract The challenge of quantum measurement, wherein observation collapses the wavefunction, hinders non-invasive study of phenomena such as entanglement and superposition. This is especially problematic when analyzing the temporal evolution of multi-qubit s…
  europepmc 2026 置信度 0.80
• Stage-aware deep learning with adversarial data synthesis enables prospective season-scale A/H1N1 influenza forecasting in China.
  Accurate influenza forecasting is essential for public health preparedness, yet many models require future covariates, provide limited interpretability, and degrade under post-pandemic regime shifts. We propose a Stage-Aware Multimodal Neural Network (SAMNN) t…
  europepmc 2027 置信度 0.80
• Kolmogorov-Arnold Networks for Sensor Data Processing: A Comprehensive Survey of Architectures, Applications, and Open Challenges.
  Kolmogorov-Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This desi…
  europepmc 2026 置信度 0.80
• Quantum-Inspired Chemical Rule for Discovering Topological Materials.
  Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, …
  europepmc 2026 置信度 0.80
• A physics-informed neural network framework for plant-wide prediction and dynamic control of wastewater treatment.
  Mechanistic models and machine learning methods provide powerful capabilities for simulating and controlling wastewater treatment processes; however, their application to real-time control faces major challenges due to the high computational cost of the former…
  europepmc 2026 置信度 0.80
• Intelligent diagnosis of major depressive disorder with edge convolution and contrastive learning.
  Based on functional connectivity (FC) matrices derived from resting-state functional magnetic resonance imaging (rs-fMRI) data, graph neural networks (GNNs), as an advanced deep learning technique, have been widely applied in major depressive disorder (MDD) di…
  europepmc 2026 置信度 0.80
• Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major.
  Children with beta-thalassemia major (β-TM) are at risk of neurodevelopmental or cognitive impairment. In this study, we developed SurfGNN, a surface-based graph neural network model, to estimate brain age from cortical morphological features extracted via str…
  europepmc 2026 置信度 0.80
• A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.
  Accurate identification of renal cell types within tissue architecture is fundamental for understanding normal kidney physiology and detecting early pathological changes. While traditional histological examination is time-intensive and dependent on expert inte…
  europepmc 2026 置信度 0.80
• High-Quality Predicted Metabolite-Pathway Annotations Expand Pathway Coverage and Increase Enriched Pathway Detection Across Metabolomics Datasets.
  Background/Objectives : Metabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highly used aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabo…
  europepmc 2026 置信度 0.80
• PoroNet: An Intrinsically Interpretable Pore Graph Neural Network for Resolving Pore-Level Adsorption in Metal-Organic Frameworks.
  Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal-organic frameworks (MOFs) for gas storage, chemical separations, and catalysis applications. The "black box" nature of these ML models, however, remains a…
  europepmc 2026 置信度 0.80
• Graph Transformers: A Survey.
  Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performance and versatility across various graph-r…
  europepmc 2026 置信度 0.80
• Neural network pruning and simultaneous feature and structure selection.
  Neural network pruning makes large neural networks more portable and smaller neural networks more interpretable with minimal loss of predictive accuracy. We propose a pruning method that reformulates the neural-network LASSO problem as a standard weighted regr…
  europepmc 2026 置信度 0.80
• The Microbiota-Gut-Brain Axis in Insomnia: Mechanisms and Intervention Strategies.
  Insomnia is one of the most common sleep disorders. Traditionally, its pathophysiology has been interpreted mainly from the perspective of the central nervous system (CNS). However, accumulating evidence suggests that the microbiota-gut-brain axis (MGBA), a bi…
  pubmed Yang M, Chen Q, Meng Z, Gu X 等 2026 Apr 1 置信度 0.82
• An Adaptive Deep Learning Framework for Multi-Label Chest X-Ray Diagnosis Using a Hybrid CNN-Transformer Architecture and Class-Wise Ensemble Fusion.
  Background/Objectives : To develop and externally evaluate a deep learning framework for multi-label thoracic disease classification on chest radiographs using hybrid convolutional neural network (CNN)-transformer architectures, hierarchical scalar-weighted fu…
  pubmed Hsieh CF, Peng HH, Tsai YH, Chang CC 等 2026 Apr 20 置信度 0.82