IEEE Journal of Biomedical and Health Informatics最新文献_第9页

MMFmiRLocEL: A multi-model fusion and ensemble learning approach for identifying miRNA subcellular localization using RNA structure language model.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-07 DOI: 10.1109/JBHI.2025.3548940

Tao Bai, Junxi Xie, Bin Liu, Yumeng Liu

{"title":"MMFmiRLocEL: A multi-model fusion and ensemble learning approach for identifying miRNA subcellular localization using RNA structure language model.","authors":"Tao Bai, Junxi Xie, Bin Liu, Yumeng Liu","doi":"10.1109/JBHI.2025.3548940","DOIUrl":"10.1109/JBHI.2025.3548940","url":null,"abstract":"MiRNA subcellular localizations (MSLs) are essential for uncovering and understanding miRNA functions in various biological processes. Several computational methods have been proposed for measuring MSL. However, existing methods only rely on manually crafted features based on sequence without considering RNA 3D structure information, and most methods often rely on single-model approaches, which fail to capture the full complexity of biological systems, further hindering predictive accuracy and performance. In this study, we introduce a deep learning-based approach, MMFmiRLocEL, which integrates multi-model fusion and ensemble learning for MSL identification. To the best of our knowledge, MMFmiRLocEL is the first method to combine sequence, structure, and function three information for MSL prediction. Specifically, it employs RNA 3D structure generated by the predicted structural model to construct a structure-based approach for MSL prediction. It also develops a sequence-based prediction method using sequence features and convolutional neural networks, while constructing a function-based prediction method using miRNA-disease association networks and deep residual neural networks. Furthermore, a multi-model fusion approach, employing weighted ensemble strategies, integrates sequence, structure, and function models to enhance the robustness and accuracy of MSL identification. Experimental results demonstrate that MMFmiRLocEL outperforms existing state-of-the-art methods, and then ablation analysis confirmed the significant contribution of the multi-model fusion mechanism to improve the prediction performance.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design, Performance Evaluation and Optimization for Intensive Care Networks Based on Non-Hierarchical Overflow Loss Systems.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-07 DOI: 10.1109/JBHI.2025.3549142

Jingjin Wu, Wei Tian, Anqi Wang, Yin-Chi Chan, Eric W M Wong, Kenny Chan, Gavin Joynt

{"title":"Design, Performance Evaluation and Optimization for Intensive Care Networks Based on Non-Hierarchical Overflow Loss Systems.","authors":"Jingjin Wu, Wei Tian, Anqi Wang, Yin-Chi Chan, Eric W M Wong, Kenny Chan, Gavin Joynt","doi":"10.1109/JBHI.2025.3549142","DOIUrl":"10.1109/JBHI.2025.3549142","url":null,"abstract":"In design and optimization of intensive care unit (ICU) networks, one common practice is to prioritize the treatment for patients of higher emergency levels, while ensuring fairness to other patients by guaranteeing a certain Quality of Service (QoS) level. One common approach to realize such priority arrangement is bed reservation policy, which designates a certain number of last occupied beds in each hospital to be exclusively used by certain patient classes. In this paper, we propose an approach that can significantly improve the computational efficiency in obtaining the optimal reservation thresholds for each patient class given their respective requirements, in a non-hierarchical ICU model (where the external emergency patients can possibly be allocated to any ICU hospital) which has been shown to be computationally challenging in performance evaluation and optimization. Specifically, we apply the Information Exchange Surrogate Approximation (IESA) to analytically approximate the key QoS metrics under given reservation thresholds, and the integer Particle Swarm Optimization (PSO) algorithm to search for the optimal threshold based on the approximation results by IESA. We demonstrate numerically, with the real data from ICUs in Hong Kong, that IESA approximation can obtain reasonably accurate results for QoS metrics, and thus lead to accurate optimal reservation thresholds. In addition, our proposed approach combining IESA and PSO can significantly reduce the computation time by more than four orders of magnitude, compared to the state-of-the-art evaluation and optimization methods in existing research for similar problems, especially for ICU networks with practical sizes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Early Parkinson's Disease by Leveraging Speech Foundation Models.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-07 DOI: 10.1109/JBHI.2025.3548917

Quang Dao, Laetitia Jeancolas, Graziella Mangone, Sara Sambin, Alize Chalancon, Manon Gomes, Stephane Lehericy, Jean-Christophe Corvol, Marie Vidailhet, Isabelle Arnulf, Dijana Petrovska Delacretaz, Mounim A El-Yacoubi

{"title":"Detection of Early Parkinson's Disease by Leveraging Speech Foundation Models.","authors":"Quang Dao, Laetitia Jeancolas, Graziella Mangone, Sara Sambin, Alize Chalancon, Manon Gomes, Stephane Lehericy, Jean-Christophe Corvol, Marie Vidailhet, Isabelle Arnulf, Dijana Petrovska Delacretaz, Mounim A El-Yacoubi","doi":"10.1109/JBHI.2025.3548917","DOIUrl":"10.1109/JBHI.2025.3548917","url":null,"abstract":"Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by a wide range of motor and non-motor symptoms. Among these symptoms, alterations in speech and voice quality stand out as early and prominent indicators of the disease. Recently, the emergence of speech foundation models has revolutionized the field by providing powerful tools for speech processing and feature extraction. In this article, we investigate the capabilities of three state-of the art speech foundation models, wav2vec2.0, Whisper and SeamlessM4T, to develop robust and accurate methods for PD detection from voice recordings. We experiment with both direct feature extraction and finetuning of the foundation models for the PD classification task, and validate the results against clinical and neuroimaging data. We achieve promising results using both pretrained features and models' finetuning, with finetuning providing stronger performance, up to 91.35% for AUC, which is the new state of the art on the ICEBERG dataset. The predictions of our models also show good correlation with clinical as well as DaTSCAN scores, proving the feasibility to apply speech foundation models for detection of early PD.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Arbitrary-Scale Super-Resolution Network for Multi-Contrast MRI With Permuted Cross-Attention.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3548696

Ming Zhao, Jia Fang, Boyang Chen

{"title":"An Arbitrary-Scale Super-Resolution Network for Multi-Contrast MRI With Permuted Cross-Attention.","authors":"Ming Zhao, Jia Fang, Boyang Chen","doi":"10.1109/JBHI.2025.3548696","DOIUrl":"10.1109/JBHI.2025.3548696","url":null,"abstract":"In magnetic resonance imaging (MRI), low-resolution (LR) images often hamper clinical diagnosis and research due to constraints in imaging conditions and technology limitations. Recent studies in super-resolution (SR) reconstruction of multi-contrast MRI have shown promise by leveraging the complementary information from different MRI contrasts. However, existing multi-contrast MRI SR techniques face several challenges: 1) a lack of pre-alignment precision can result in distorted reconstructions; 2) prevailing transformer network structures, with their smaller windows (e.g., 8×8), struggle to effectively capture long-range dependencies and lack the ability to interact between different windows; and 3) current methods are limited to fixed integer scaling (e.g., 2×, 3×, 4×), which limits flexibility and increases complexity in training and storage. To address these challenges, we propose a novel arbitrary-scale SR network for multi-contrast MRI. Specifically, our approach compensates for spatial misalignment between modalities through deformable registration module and employs permuted cross-attention transformer in MR images. In addition, we introduce a ref-scale ensemble implicit attention module that better integrates high-frequency information from reference images and enables arbitrary-scale upsampling. Extensive experiments on two publicly available MRI datasets validate the superiority of our method in multi-contrast MRI SR, demonstrating its significant potential in clinical applications. Our code is available at https://github.com/fangxiaojia0/An-Arbitrary-Scale-Super-Resolution-Network-for-Multi-Contrast-MRI-With-Permuted-Cross-Attention.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3540507

Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R KhudaBukhsh, Liviu P Dinu

{"title":"On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook.","authors":"Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R KhudaBukhsh, Liviu P Dinu","doi":"10.1109/JBHI.2025.3540507","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3540507","url":null,"abstract":"Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AGPred: An End-to-End Deep Learning Model to Predicting Drug Approvals in Clinical Trials Based on Molecular Features.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3547315

Haochen Zhao, Xiao Liang, Chenliang Xie, Shaokai Wang

{"title":"AGPred: An End-to-End Deep Learning Model to Predicting Drug Approvals in Clinical Trials Based on Molecular Features.","authors":"Haochen Zhao, Xiao Liang, Chenliang Xie, Shaokai Wang","doi":"10.1109/JBHI.2025.3547315","DOIUrl":"10.1109/JBHI.2025.3547315","url":null,"abstract":"One of the major challenges in drug development is maintaining acceptable levels of efficacy and safety throughout the various stages of clinical trials and successfully bringing the drug to market. However, clinical trials are time-consuming and expensive. While there are computational methods designed to predict the likelihood of a drug passing clinical trials and reaching the market, these methods heavily rely on manual feature engineering and cannot automatically learn drug molecular representations, resulting in relatively low model performance. In this study, we propose AGPred, an attention-based deep Graph Neural Network (GNN) designed to predict drug approval rates in clinical trials accurately. Unlike the few existing studies on drug approval prediction, which only use predicted targets of compounds, our novel approach employs a GNN module to extract high-potential features of compounds based on their molecular graphs. Additionally, a cross-attention-based fusion module is utilized to learn molecular fingerprint features, enhancing the model's representation of chemical structures. Meanwhile, AGPred integrates the physicochemical properties of drugs to provide a comprehensive description of the molecules. Experimental results indicate that AGPred outperforms four state-of-the-art models on both benchmark and independent datasets. The study also includes several ablation experiments and visual analyses to demonstrate the effectiveness of our method in predicting drug approval during clinical trials. The codes for AGPred are available at https://github.com/zhc940702/AGPred.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editorial:Application of Computational Techniques in Drug Discovery and Disease Treatment

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3537213

Xing Chen;Qi Zhao

引用次数: 0

Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3540937

Hugo Ramirez, Davide Tabarelli, Arianna Brancaccio, Paolo Belardinelli, Elisabeth B Marsh, Michael Funke, John C Mosher, Fernando Maestu, Mengjia Xu, Dimitrios Pantazis

{"title":"Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories.","authors":"Hugo Ramirez, Davide Tabarelli, Arianna Brancaccio, Paolo Belardinelli, Elisabeth B Marsh, Michael Funke, John C Mosher, Fernando Maestu, Mengjia Xu, Dimitrios Pantazis","doi":"10.1109/JBHI.2025.3540937","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3540937","url":null,"abstract":"Characterizing age-related alterations in brain networks is crucial for understanding aging trajectories and identifying deviations indicative of neurodegenerative disorders, such as Alzheimer's disease. In this study, we developed a Fully Hyperbolic Neural Network (FHNN) to embed functional brain connectivity graphs derived from magnetoencephalography (MEG) data into low dimensions on a Lorentz model of hyperbolic space. Using this model, we computed hyperbolic embeddings of the MEG brain networks of 587 individuals from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Notably, we leveraged a unique metric-the radius of the node embeddings-which effectively captures the hierarchical organization of the brain, to characterize subtle hierarchical organizational changes in various brain subnetworks attributed to the aging process. Our findings revealed that a considerable number of subnetworks exhibited a reduction in hierarchy during aging, with some showing gradual changes and others undergoing rapid transformations in the elderly. Moreover, we demonstrated that hyperbolic features outperform traditional graph-theoretic measures in capturing age-related information in brain networks. Overall, our study represents the first evaluation of hyperbolic embeddings in MEG brain networks for studying aging trajectories, shedding light on critical regions undergoing significant age-related alterations in the large cohort of the Cam-CAN dataset.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Journal of Biomedical and Health Informatics Publication Information

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3541762

引用次数: 0

A Blockchain-Enabled AI-Driven Secure Searchable Encryption Framework for Medical IoT Systems.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3538623

Salabat Khan, Mansoor Khan, Muhammad Asghar Khan, Muhammad Attique Khan, Lu Wang, Kaishun Wu

{"title":"A Blockchain-Enabled AI-Driven Secure Searchable Encryption Framework for Medical IoT Systems.","authors":"Salabat Khan, Mansoor Khan, Muhammad Asghar Khan, Muhammad Attique Khan, Lu Wang, Kaishun Wu","doi":"10.1109/JBHI.2025.3538623","DOIUrl":"10.1109/JBHI.2025.3538623","url":null,"abstract":"Blockchain technology is widely adopted in the Internet of Medical Things (IoMT) for information storage and retrieval. The integration of blockchain with IoMT systems enhances security; however, it raises privacy and security in data searching and storage. This study proposes a novel Binary Spring Search (BSS) technique based on group theory and integrated with a hybrid deep neural network approach to enhance the security and trustworthiness of IoMT. The proposed method incorporates secure key revocation and dynamic policy updates. The proposed framework leverages blockchain technology for immutable and decentralized data management, Artificial Intelligence (AI) for dynamic data analysis and threat detection, and advanced searchable encryption techniques to facilitate secure and efficient data queries. The proposed patient-centered data access model that combines blockchain technology with trust chains makes our method safer and more efficient and demonstrates a return on investment. Furthermore, our blockchain-based architecture ensures the integrity and immutability of medical data generated by IoMT devices, allowing for decentralized and tamper-proof storage. We used the hyper-ledger fabric tool, known as OrigionLab, for simulations in a blockchain context. We claim that the suggested framework provides a more searchable and secure solution to the healthcare system when compared to the other methods given through our findings. The simulation results show that our algorithm significantly reduces transaction time while maintaining high levels of security, making it a robust solution for managing Patient Health Records (PHR) in a decentralized manner.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0