IEEE Journal of Biomedical and Health Informatics最新文献_第5页

Multistage Diffusion Model With Phase Error Correction for Fast PET Imaging. 基于相位误差校正的PET快速成像多级扩散模型。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3567645

Yunlong Gao, Zhenxing Huang, Xingyu Xie, Wenjie Zhao, Qianyi Yang, Xinlan Yang, Yongfeng Yang, Hairong Zheng, Dong Liang, Jianjun Liu, Ruohua Chen, Zhanli Hu

{"title":"Multistage Diffusion Model With Phase Error Correction for Fast PET Imaging.","authors":"Yunlong Gao, Zhenxing Huang, Xingyu Xie, Wenjie Zhao, Qianyi Yang, Xinlan Yang, Yongfeng Yang, Hairong Zheng, Dong Liang, Jianjun Liu, Ruohua Chen, Zhanli Hu","doi":"10.1109/JBHI.2025.3567645","DOIUrl":"10.1109/JBHI.2025.3567645","url":null,"abstract":"Fast PET imaging is clinically important for reducing motion artifacts and improving patient comfort. While recent diffusion-based deep learning methods have shown promise, they often fail to capture the true PET degradation process, suffer from accumulated inference errors, introduce artifacts, and require extensive reconstruction iterations. To address these challenges, we propose a novel multistage diffusion framework tailored for fast PET imaging. At the coarse level, we design a multistage structure to approximate the temporal non-linear PET degradation process in a data-driven manner, using paired PET images collected under different acquisition duration. A Phase Error Correction Network (PECNet) ensures consistency across stages by correcting accumulated deviations. At the fine level, we introduce a deterministic cold diffusion mechanism, which simulates intra-stage degradation through interpolation between known acquisition durations-significantly reducing reconstruction iterations to as few as 10. Evaluations on [68Ga]FAPI and [18F]FDG PET datasets demonstrate the superiority of our approach, achieving peak PSNRs of 36.2 dB and 39.0 dB, respectively, with average SSIMs over 0.97. Our framework offers high-fidelity PET imaging with fewer iterations, making it practical for accelerated clinical imaging.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7373-7386"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964995","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

Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation. 快速ddpm：用于医学图像到图像生成的快速去噪扩散概率模型。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3565183

Hongxu Jiang, Muhammad Imran, Teng Zhang, Yuyin Zhou, Muxuan Liang, Kuang Gong, Wei Shao

{"title":"Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation.","authors":"Hongxu Jiang, Muhammad Imran, Teng Zhang, Yuyin Zhou, Muxuan Liang, Kuang Gong, Wei Shao","doi":"10.1109/JBHI.2025.3565183","DOIUrl":"10.1109/JBHI.2025.3565183","url":null,"abstract":"Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for disease diagnosis and treatment planning. This is primarily due to the high computational cost associated with the use of large number of time steps (e.g., 1,000) in diffusion processes. Training a diffusion model on medical images typically takes days to weeks, while sampling each image volume takes minutes to hours. To address this challenge, we introduce Fast-DDPM, a simple yet effective approach capable of simultaneously improving training speed, sampling speed, and generation quality. Unlike DDPM, which trains the image denoiser across 1,000 time steps, Fast-DDPM trains and samples using only 10 time steps. The key to our method lies in aligning the training and sampling procedures to optimize time-step utilization. Specifically, we introduced two efficient noise schedulers with 10 time steps: one with uniform time step sampling and another with non-uniform sampling. We evaluated Fast-DDPM across three medical image-to-image generation tasks: multi-image super-resolution, image denoising, and image-to-image translation. Fast-DDPM outperformed DDPM and current state-of-the-art methods based on convolutional networks and generative adversarial networks in all tasks. Additionally, Fast-DDPM reduced the training time to 0.2× and the sampling time to 0.01× compared to DDPM.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7326-7335"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011672","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

Morphology Prior Enhanced Teeth Segmentation for High-Resolution Oral Scans. 形态学先验增强高分辨率口腔扫描的牙齿分割。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3616205

Yuxian Jiang, Xiuying Wang, Tao Yang, Changkai Ji, Lanshan He, Yusheng Liu, Wei Wang, Min Liu, Junyu Shi, Huayan Guo, Sheng Chen, Lisheng Wang

{"title":"Morphology Prior Enhanced Teeth Segmentation for High-Resolution Oral Scans.","authors":"Yuxian Jiang, Xiuying Wang, Tao Yang, Changkai Ji, Lanshan He, Yusheng Liu, Wei Wang, Min Liu, Junyu Shi, Huayan Guo, Sheng Chen, Lisheng Wang","doi":"10.1109/JBHI.2025.3616205","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3616205","url":null,"abstract":"Deep learning methods have been proposed for tooth segmentation on high-resolution intra-oral scans (IOS) that plays a crucial role in clinical dental practice. However, they generally segment teeth in a low-resolution data with a fixed receptive field and generate final segmentation by up-sampling interpolation, and neglect teeth's morphology priors: their similar dental arch structures and significantly different curvatures in different parts of each tooth. They thus lack adaptability to different parts of each tooth, and show less accurate segmentation of boundary points between teeth and gums due to the up-sampling computation. Further, cluttered poses of IOS limit their generalization and usability of teeth location and geometric information. To address these limitations, a morphology prior enhanced teeth segmentation framework is proposed in this paper. Firstly, a robust preprocessing is introduced to align poses of different IOS by computing their dental arch orientations, thereby improving segmentation generalization and usability of IOS geometric information. Secondly, a decomposition-merging strategy is designed to avoid the up-sampling limitation, which decomposes an IOS into multiple low-resolution data and merges their segmentation outcomes into a high-resolution result. Thirdly, an innovative module integrating semantic and geometric features is proposed to adaptively select deformable receptive fields. It geometrically samples within a variable probability space to construct receptive fields with varied graph relationships for different points, facilitating adaptive segmentation of different parts of each tooth. Experimental results on 6238 IOS from four centers demonstrate that our method significantly outperforms 11 state-of-the-art methods, achieving a 6.93% enhancement for cross-center testing.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206258","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

Whole Heart Segmentation Based on 3D Contour-Guided Multi-Head Attention Network From CT and MRI Images. 基于CT和MRI图像的三维轮廓引导多头注意网络的全心分割。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3584074

Feiyan Li, Weisheng Li, Yidong Peng, Yucheng Shu

{"title":"Whole Heart Segmentation Based on 3D Contour-Guided Multi-Head Attention Network From CT and MRI Images.","authors":"Feiyan Li, Weisheng Li, Yidong Peng, Yucheng Shu","doi":"10.1109/JBHI.2025.3584074","DOIUrl":"10.1109/JBHI.2025.3584074","url":null,"abstract":"Heart image segmentation is a critical task in medical image processing, which is crucial for the diagnosis and treatment planning of cardiovascular diseases. It helps doctors understand patients' cardiac anatomy and functional status more comprehensively and lays the foundation for personalized medicine and precision medicine research. Addressing the current challenges of rough surfaces on the entire heart, incomplete segmentation of heart substructures, and the lack of structured prediction of pulmonary arteries due to artifacts, scale diversity, uneven intensity, and boundary ambiguity in cardiac computed tomography (CT) and magnetic resonance imaging (MRI) images, we propose a whole heart segmentation algorithm based on 3D contour guided network. The proposed algorithm achieves robust whole heart segmentation results and has few network structure parameters. To enhance the consistency of features extracted by the codec, we propose a 3D codec information integration module to focus on task-related areas. In the final stage of information integration, features of different scales are combined. A 3D contour attention module enhances the perception of the heart's structure and shape. Contour prediction results from the initial stage, generating a low-resolution voxel of the entire heart with contour details. The second stage builds upon the initial phase of secondary learning to achieve multi-label segmentation results. The proposed algorithm achieved average Dice scores of 0.905 and 0.865 for the CT and MRI modalities, respectively, in 40 cases.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7459-7472"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511818","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

A Lightweight ML-Based ECG Classification System Using Self-Personalized Anomaly Detector. 基于自个性化异常检测器的轻量级ml心电分类系统。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3589142

Sunwoo Yoo, Seungwoo Hong, Dongyun Kam, Youngjoo Lee

{"title":"A Lightweight ML-Based ECG Classification System Using Self-Personalized Anomaly Detector.","authors":"Sunwoo Yoo, Seungwoo Hong, Dongyun Kam, Youngjoo Lee","doi":"10.1109/JBHI.2025.3589142","DOIUrl":"10.1109/JBHI.2025.3589142","url":null,"abstract":"Targeting the real-time arrhythmia diagnosis on resource-limited edge devices, in this paper, we present a lightweight electrocardiogram classification system using event-driven machine learning processing. A self-personalized anomaly detector based on signal processing is newly developed to dynamically update internal decision criteria from each patient's recent electrocardiogram history, that activates the following machine learning model only for the abnormal cases. A Siamese neural network is adopted to identify detailed arrhythmia classes by comparing features from the self-personalized normal data and the current abnormal input, increasing the classification accuracy. We also develop a simple version of our Siamese model to reduce the number of trainable parameters while preserving the end-to-end classification accuracy. Experimental results show that the proposed event-driven system reduces ML model activations by 74% for normal beats, achieving a classification accuracy of 96.9% comparable to leading solutions. Additionally, it consumes three times less energy and achieves 3.6 times faster processing latency compared to cost-aware method on a mobile GPU platform, enabling extended battery life and real-time analysis on edge devices.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7274-7284"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144636931","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

TDFormer: Top-Down Token Generation for 3D Medical Image Segmentation. TDFormer：用于3D医学图像分割的自上而下令牌生成。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3567590

Hao Du, Qihua Dong, Yan Xu, Jing Liao

{"title":"TDFormer: Top-Down Token Generation for 3D Medical Image Segmentation.","authors":"Hao Du, Qihua Dong, Yan Xu, Jing Liao","doi":"10.1109/JBHI.2025.3567590","DOIUrl":"10.1109/JBHI.2025.3567590","url":null,"abstract":"Accurate medical image segmentation is critical to effective treatment strategies. Existing transformer-based methods for image segmentation mostly split the input image into a fixed and regular grid and regard cells in the grid as the vision tokens. However, not all tokens are of equal importance in the medical segmentation tasks, e.g., the tokens in tumor areas must be processed in a higher resolution than the background tokens which can be easily predicted with fewer transformer layers. In this paper, we propose a simple yet efficient segmentation framework called Top-Down Transformer (TDFormer), which incorporates a spatially adaptive token generation scheme into the transformer. The proposed top-down token generation comprises the following three components: attentiveness calculation, token splitting, and token fusion, where the collaboration of these components gradually fuses redundant background tokens and focuses only on the most critical areas. This allows for allocating more computation to process tokens containing delicate details in a finer resolution. Extensive experiments are conducted to demonstrate the robustness and effectiveness of the proposed TDFormer, that our method are superior to other state-of-the-art methods on the following publicly accessible datasets: BTCV Challenge, LiTS and BraTS 2020. We also dissect our method and evaluate the performance of each component.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7359-7372"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010006","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

SliceMamba With Neural Architecture Search for Medical Image Segmentation. 基于神经结构搜索的SliceMamba医学图像分割。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3564381

Chao Fan, Hongyuan Yu, Yan Huang, Liang Wang, Zhenghan Yang, Xibin Jia

{"title":"SliceMamba With Neural Architecture Search for Medical Image Segmentation.","authors":"Chao Fan, Hongyuan Yu, Yan Huang, Liang Wang, Zhenghan Yang, Xibin Jia","doi":"10.1109/JBHI.2025.3564381","DOIUrl":"10.1109/JBHI.2025.3564381","url":null,"abstract":"Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These local features are crucial for medical image segmentation as they provide critical structural information about lesions and organs. To address this limitation, we propose SliceMamba, a simple yet effective locally sensitive Mamba-based medical image segmentation model. SliceMamba features an efficient Bidirectional Slicing and Scanning (BSS) module, which performs bidirectional feature slicing and employs varied scanning mechanisms for sliced features with distinct shapes. This design keeps spatially adjacent features close in the scan sequence, preserving the local structure of the image and enhancing segmentation performance. Additionally, to fit the varying sizes and shapes of lesions and organs, we introduce an Adaptive Slicing Search method that automatically identifies the optimal feature slicing method based on the characteristics of the target data. Extensive experiments on two skin lesion datasets (ISIC2017 and ISIC2018), two polyp segmentation datasets (Kvasir and ClinicDB), one ultra-wide field retinal hemorrhage segmentation dataset (UWF-RHS), and one multi-organ segmentation dataset (Synapse) demonstrate the effectiveness of our method.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7446-7458"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011804","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

LM-TLIPs: Integrating Large Model and Transfer Learning Technology for Precise Identification of Phosphorylation Sites in SARS-CoV-2. LM-TLIPs：整合大模型和迁移学习技术精确识别SARS-CoV-2磷酸化位点。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3561124

Yun Zuo, Minquan Wan, Xinyue Shao, Dandan Qiao, Bulanni Xiong, Zhaohong Deng

{"title":"LM-TLIPs: Integrating Large Model and Transfer Learning Technology for Precise Identification of Phosphorylation Sites in SARS-CoV-2.","authors":"Yun Zuo, Minquan Wan, Xinyue Shao, Dandan Qiao, Bulanni Xiong, Zhaohong Deng","doi":"10.1109/JBHI.2025.3561124","DOIUrl":"10.1109/JBHI.2025.3561124","url":null,"abstract":"In recent years, the rapid spread of SARS-CoV-2 has triggered a global health crisis and socio-economic challenges. As a crucial post-translational modification, phosphorylation plays a vital role in the regulation of cellular functions. Given its close relationship with SARS-CoV-2 infection, accurately identifying virus-induce phosphorylation sites is essential for understanding the molecular mechanisms of viral infection and its impact on host cells. Although the development of various computational tools for predicting phosphorylation sites, these tools have several shortcomings, such as insufficient data and limited model generalization ability, which limit their effectiveness in practical applications. To overcome these limitations, this study proposes a novel method for predicting SARS-CoV-2 phosphorylation sites, LM-TLIPs, based on the latest technology. This method uses the most advanced large model technology ESM-2 to extract information from S/T sites and Y sites; by fine-tuning the large model and introducing transfer learning technology, it addresses the challenge of accurately predicting Y sites due to insufficient data in this study. Independent testing on S/T sites(Acc:0.8309, Sn:0.8443, Sp:0.8174, MCC:0.6620, AUC:0.8993) and Y sites(Acc:0.9048, Sn:0.9524, Sp:0.8571, MCC:0.8132, AUC:0.9388) has validated that LM-TLIPs outperforms existing optimal prediction tools, demonstrating its superior ability in identifying phosphorylation sites. Furthermore, we conducted an exhaustive interpretability analysis based on attention weight heatmaps and feature importance ranking to enhance the transparency and confidence of prediction results.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"6982-6989"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963282","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

BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction. BFGTP: bert引导的毒性预测的两阶段分子表示学习框架。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3556766

Kaimiao Hu, Yuan He, Jianguo Wei, Changming Sun, Jie Geng, Leyi Wei, Ran Su

{"title":"BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction.","authors":"Kaimiao Hu, Yuan He, Jianguo Wei, Changming Sun, Jie Geng, Leyi Wei, Ran Su","doi":"10.1109/JBHI.2025.3556766","DOIUrl":"10.1109/JBHI.2025.3556766","url":null,"abstract":"Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"6960-6970"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763723","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

PKAN: Leveraging Kolmogorov-Arnold Networks and Multi-Modal Learning for Peptide Prediction With Advanced Language Models. PKAN：利用Kolmogorov-Arnold网络和多模态学习与高级语言模型进行肽预测。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-10-01 DOI: 10.1109/JBHI.2025.3561846

Li Wang, Xiangzheng Fu, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng, Yiping Liu

{"title":"PKAN: Leveraging Kolmogorov-Arnold Networks and Multi-Modal Learning for Peptide Prediction With Advanced Language Models.","authors":"Li Wang, Xiangzheng Fu, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng, Yiping Liu","doi":"10.1109/JBHI.2025.3561846","DOIUrl":"10.1109/JBHI.2025.3561846","url":null,"abstract":"Peptides can offer highly specific biological activities, serving as essential mediators of intercellular signaling, which are critical for advancing precision medicine and drug development. Their primary structure can be depicted either as an amino acid sequence or as a chemical molecules consisting of atoms and chemical bonds. Large language models (LLMs) hold the potential to thoroughly elucidate the intricate intrinsic properties of peptides. Here we present the Peptide Kolmogorov-Arnold Network (PKAN), a framework leveraging multi-modal representations inspired by advanced language models for peptide activity and functionality prediction. Comparative experiments across tasks show that PKAN outperforms state-of-the-art models while maintaining a streamlined design with superior predictive capabilities. The multi-modal feature importance scoring, anchored in global structures and the significant marginal impacts of derived features on the model, coupled with intricate symbolic regression of specific activation functions, further demonstrates the robustness and precision of the PKAN framework in identifying and elucidating key determinants of peptide functionality. This work provides scientific evidence for investigating the complex mechanisms of peptide materials and supports the progression of peptide language paradigms in biology.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":"7000-7009"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009358","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