IEEE Journal of Biomedical and Health Informatics最新文献

The cell cycle plays a pivotal role in regulating cell fate and stem cell differentiation. As a rate-limiting step in differentiation, its precise regulation is essential for maintaining cellular diversity and tissue homeostasis. Recent advances in single-cell multi-omics technologies have enabled the integration of gene expression data and chromatin structural regulation, thereby enhancing the prediction of cell cycle using multi-omics approaches. However, current algorithms have yet to effectively integrate transcriptome and three-dimensional (3D) genomic data for cell cycle prediction. We propose MomicPred, an innovative dual-branch multi-modal fusion framework designed to predict cell cycle dynamics. This framework integrates transcriptome-derived gene expression data with global chromatin structural insights from 3D genome data. By leveraging the complementary nature of these multi-omics data, MomicPred extracts three core feature sets that uncover cross-layer associations and synergistic interactions between the two omics modalities, enabling high-precision cell cycle prediction. We further evaluate the framework's performance through various benchmarking strategies, demonstrating its efficiency and robustness. Furthermore, feature importance analysis reveals chromatin structural changes and key biological processes across distinct cell cycle stages, offering new perspectives for future research.

Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections. MneGCL first performs semantic clustering of drugs and targets by identifying strongly correlated nodes in the semantic similarity network to construct semantic contrastive prototypes, while simultaneously establishing phenotypic prototypes based on the Gaussian interaction profile kernel similarity. These complementary views are then combined through neighborhood-enhanced contrastive learning to effectively capture latent homogeneous features and enhance representation learning for sparse nodes in heterogeneous graphs, with final predictions generated through a graph autoencoders framework. Comparative experimental results demonstrate that MneGCL achieves superior performance across three benchmark datasets, with particularly notable improvements on the highly sparse DrugBank dataset, showing an average $2.5 %$ increase to baseline models. Additional experiments further validate the effectiveness of MneGCL in enriching feature representations for sparsely connected nodes.

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse effects and compromised therapeutic efficacy. Accurate prediction of DDI events, which involve not only identifying interacting drug pairs but also characterizing the specific nature and context of their interactions, is essential for drug safety and personalized medicine. In this study, we propose a novel Multi-view Contrastive Learning framework, namely MCL-DDI, for DDI Event Prediction by leveraging multi-view representations of drugs to enhance predictive performance. MCL-DDI integrates molecular structures and network features, capturing complementary information about drug properties and interactions. By employing contrastive learning, we align and unify drug representations across these diverse views, enabling the framework to distinguish complex interaction patterns. Extensive experiments on benchmark datasets demonstrate that MCL-DDI outperforms state-of-the-art methods in terms of predictive accuracy. Furthermore, case studies highlight the model's ability to identify clinically relevant DDIs, offering practical insights for drug development and risk assessment. Our work establishes a robust and accurate paradigm for DDI event prediction, paving the way for safer and more effective pharmacological interventions.

The metaverse, which integrates physical and virtual realities through technologies such as high-speed internet, virtual and augmented reality, and artificial intelligence (AI), offers transformative prospects across various fields, particularly healthcare. This integration introduces a new paradigm in AI-driven medical imaging, particularly in assessing brain age-a crucial marker for detecting age-related neuropathologies such as Alzheimer's disease (AD) using magnetic resonance imaging (MRI). Despite advances in deep learning for estimating brain age from structural MRI (sMRI), incorporating functional MRI (fMRI) data presents significant challenges due to its complex data structure and the noisy nature of functional connectivity measurements. To address these challenges, we present the Multitask Adversarial Variational Autoencoder (M-AVAE), a bespoke deep learning framework designed to enhance brain age predictions through multimodal MRI data integration. The M-AVAE uniquely separates latent variables into generic and unique codes, effectively isolating shared and modality-specific features. Additionally, integrating multitask learning with sex classification as a supplementary task enables the model to account for sex-specific aging nuances. Evaluated on the OpenBHB dataset-a comprehensive multisite brain MRI aggregation-the M-AVAE demonstrates exceptional performance, achieving a mean absolute error of 2.77 years, surpassing conventional methodologies. This success positions M-AVAE as a powerful tool for metaverse-based healthcare applications in brain age estimation.

Enhancing the resolution of Magnetic Resonance Imaging (MRI) through super-resolution (SR) reconstruction is crucial for boosting diagnostic precision. However, current SR methods primarily rely on single LR images or multi-contrast features, limiting detail restoration. Inspired by video frame interpolation, this work utilizes the spatiotemporal correlations between adjacent slices to reformulate the SR task of anisotropic 3D-MRI image into the generation of new high-resolution (HR) slices between adjacent 2D slices. The generated SR slices are subsequently combined with the HR adjacent slices to create a new HR 3D-MRI image. We propose a innovative network architecture termed DGWMSR, comprising a backbone network and a feature supplement module (FSM). The backbone's core innovations include the displacement former block (DFB) module, which independently extracts structural and displacement features, and the mask-displacement vector network (MDVNet) which combines with Warp mechanism to facilitate edge pixel detailing. The DFB integrates the inter-slice attention (ISA) mechanism into the Transformer, effectively minimizing the mutual interference between the two types of features and mitigating volume effects during reconstruction. Additionally, the FSM module combines self-attention with feed-forward neural network, which emphasizes critical details derived from the backbone architecture. Experimental results demonstrate the DGWMSR network outperforms current MRI SR methods on Kirby21, ANVIL-adult, and MSSEG datasets.

Accurate segmentation of endoscopic instruments is essential in robot-assisted surgery, supporting precis enavigation, enhancing safety, and advancing surgical automation. However, this task is challenging due to factors like complex environments, instrument-tissue similarity, and lighting variations. Instruments, due to their material properties, have distinct depth distributions compared to surrounding tissues. This aspect is often overlooked in monocular video segmentation methods.To address this issue, we propose EISegNet, a multi-task framework that prioritizes instrument segmentation with an auxiliary disparity estimation task. The framework integrates an asymmetric cross-attention mechanism to enhance segmentation performance by fusing features from both tasks. Moreover, by leveraging the geometric properties of motion, EISegNet adapts the stereo disparity estimation strategy for dual-view depth estimation, broadening its applicability to various endoscopic surgeries beyond laparoscopic procedures. Furthermore, EISegNet incorporates a Gaussian-weighted loss function to emphasize edge features, which are particularly challenging for disparity estimation. This function reduces overall loss and improves segmentation accuracy. Extensive cross-dataset experiments demonstrate the superior accuracy and generalization of our method, achieving a 5.97% increase in IoU (Intersection over Union). Qualitative evaluations on clinical datasets further demonstrate the promising performance in real-world scenarios.

Acoustic features are crucial behavioral indicators for depression detection. However, prior speech-based depression detection methods often overlook the variability of emotional patterns across samples, leading to interference from speaker identity and hindering the effective extraction of emotional changes. To address this limitation, we developed the Emotional Word Reading Experiment (EWRE) and introduced a method combining self-supervised and supervised learning for depression detection from speech called MFE-Former. First, we generate fine-grained emotional representations for response segments by computing cosine similarity between intra-sample and inter-sample contexts. Concurrently, orthogonality constraints decouple identity information from emotional features, while a Transformer decoder reconstructs spectral structures to improve sensitivity to depression-related emotional patterns. Next, we propose a multi-scale emotion change perception module and a Bernoulli distribution-based joint decision module integrate multi-level information for depression detection. By enhancing the distribution differences among positive, neutral, and negative emotional features, we find that patients with depression are more inclined to express negative emotions, whereas healthy individuals express more positive emotions. The experimental results on EWRE and AVEC 2014 show that MFE-Former outperforms state-of-the-art temporal methods under conditions of variability in emotional patterns across samples.

Retinal degenerative diseases such as age-related macular degeneration and retinitis pigmentosa cause severe vision impairment, while current electrical stimulation therapies are limited by poor spatial targeting precision. As a promising non-invasive alternative, the efficacy of temporal interference stimulation (TIS) for retinal targeting depends on optimized multi-electrode parameters. This study reconstructed a whole-head finite element model with detailed ocular structures and applied reinforcement learning (RL)-based multi-channel electrode parameter optimization to retinal stimulation. Systematic evaluation demonstrated that the focal precision of TIS improves with increasing channel numbers (consistent across all subject head models), with RL significantly outperforming conventional genetic algorithms (GA) and unsupervised neural networks (USNN) in focusing capability. Furthermore, by implementing the computationally intensive envelope calculation using the JAX framework, we achieved a nearly order-of-magnitude reduction in optimization time (to approx. 2 minutes per run on an RTX 4090D), significantly enhancing the practical feasibility of the proposed RL framework. This work provides a novel and computationally efficient methodology for precise non-invasive neuromodulation parameter optimization, applicable not only to retinal diseases but potentially to broader neurological conditions.

Sepsis is a major cause of ICU mortality, where early recognition and effective interventions are essential for improving patient outcomes. However, the vasoactive-inotropic score (VIS) varies dynamically with a patient's hemodynamic status, complicated by irregular medication patterns, missing data, and confounders, making sepsis prediction challenging. To address this, we propose a novel Teacher-Student multitask framework with self-supervised VIS pretraining via a Masked Autoencoder (MAE). The teacher model performs mortality classification and severity-score regression, while the student distills robust time-series representations, enhancing adaptation to heterogeneous VIS data. Compared to LSTM-based methods, our approach achieves an AUROC of 0.829 on MIMIC-IV 3.0 (9,476 patients), outperforming the baseline (0.74). SHAP analysis revealed that SOFA score (0.147) had the greatest impact on ICU mortality, followed by LODS (0.033), single marital status (0.031), and Medicaid insurance (0.023), highlighting the role of sociodemographic factors. SAPSII (0.020) also contributed significantly. These findings suggest that clinical and social factors should be considered in ICU decision-making. Our multitask and distillation strategies enable earlier identification of high-risk patients, improving prediction accuracy and disease management, offering new tools for ICU decision support.

As the Internet of Things (IoT) and artificial intelligence (AI) technologies are rapidly evolving, smart healthcare has emerged as a transformative solution to enhance healthcare quality and optimize resource allocation. This study introduces FEI-Hi, a federated edge intelligence paradigm that integrates edge computing with federated learning (FL) to enable secure and efficient medical data processing. FEI-Hi comprises three principal layers: FL layer, which facilitates cross-device collaborative training through encrypted model updates; aggregation layer, which refines the global model by consolidating updates; and edge layer, which performs local data processing and model inference. FEI-Hi leverages distributed intelligent computation, model parameter compression, and efficient node clustering to enhance the accuracy and efficiency of medical data processing significantly. By employing Wasserstein distance for clustering and parameter selection, FEI-Hi ensures model convergence and stability. Experimental results on multiple medical datasets demonstrate a 30% improvement in the model training speed and an F1-score exceeding 90%, surpassing the state-of-the-art (SOTA) benchmarks in model parameter transfer efficiency, training speed, and accuracy.