Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第9页

MRI-based Biomarkers for a Liver Fibrosis and Steatosis Detection and Stratification in Patients with Non-Alcoholic Fatty Liver Disease. 非酒精性脂肪肝患者肝纤维化和脂肪变性检测和分层的mri生物标志物

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11252843

Josep Vicent Castello-Barrachina, Pilar Castellote-Huguet, Juan Antonio Romero-Martin, Jose Manuel Santabarbara, Alicia M Maceira, David Moratal

{"title":"MRI-based Biomarkers for a Liver Fibrosis and Steatosis Detection and Stratification in Patients with Non-Alcoholic Fatty Liver Disease.","authors":"Josep Vicent Castello-Barrachina, Pilar Castellote-Huguet, Juan Antonio Romero-Martin, Jose Manuel Santabarbara, Alicia M Maceira, David Moratal","doi":"10.1109/EMBC58623.2025.11252843","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252843","url":null,"abstract":"The diagnosis of liver diseases such as fibrosis and steatosis has traditionally relied on invasive techniques like liver biopsy. This study investigates hepatic biomarkers derived from magnetic resonance imaging (MRI) to non-invasively detect and stratify liver fibrosis and steatosis. Parameters such as liver stiffness, fat fraction, T2 relaxation time, and diffusion coefficients (apparent diffusion coefficient -ADC- and intravoxel incoherent motion -IVIM-) were analyzed in a cohort of 27 patients suspected of metabolic dysfunction-associated steatotic liver disease (MASLD), using a 3T MR scanner. Results show ADC as a promising fibrosis biomarker with a significant negative correlation to liver stiffness (r = -0.6801, p-value = 0.0000953), while T2 relaxation time correlated positively with fat fraction (r = 0.6087, p-value = 0.0007536). No significant correlation was found between PDFF and fibrosis, and IVIM parameters showed limited predictive value. These findings highlight the potential of MRI biomarkers for non-invasive liver disease assessment.Clinical Relevance- The findings of this study highlight the potential of non-invasive MRI-derived biomarkers for the diagnosis and stratification of liver fibrosis and steatosis. By reducing reliance on invasive procedures such as liver biopsy, these biomarkers could improve clinical outcomes, enable earlier detection, and minimize the risks and economical costs associated with traditional diagnostic methods.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Emotion-Brain Representations With Orthogonal Fuzzy Power-Coherence Alignment. 正交模糊功率相干对齐增强情绪-脑表征。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11252717

Yu Pei, Dongdong Zhou, Shaokai Zhao, Chuang Ma, Liang Xie, Erwei Yin

{"title":"Enhancing Emotion-Brain Representations With Orthogonal Fuzzy Power-Coherence Alignment.","authors":"Yu Pei, Dongdong Zhou, Shaokai Zhao, Chuang Ma, Liang Xie, Erwei Yin","doi":"10.1109/EMBC58623.2025.11252717","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252717","url":null,"abstract":"Electroencephalogram (EEG)-based emotion recognition is a cornerstone of affective computing and human-computer interaction. However, progress in this field remains limited by the inherent fuzziness of emotions and the high complexity of EEG signals. To address these challenges, we propose Orthogonal Fuzzy Power-Coherence Alignment (OFPCA), a novel framework designed to enhance emotional brain representation learning. To effectively model the fuzzy nature of emotions, OFPCA employs a Takagi-Sugeno-Kang (TSK) fuzzy system as a feature learner. To overcome the complexity of EEG signals, it aligns two complementary perspectives-the local-energy view and the cross-regional interaction view-through contrastive learning, enabling a more comprehensive EEG representation. The local-energy view is represented by power spectral density (PSD), while the cross-regional interaction view is captured by coherence (COH). To ensure robust emotional brain representation learning, we introduce orthogonal regularization loss into OFPCA's training process. We evaluate OFPCA on two benchmark datasets (SEED with three emotions and CRED with five emotions) under two evaluation settings. Under the cross-subject setting, OFPCA achieves 41.14% on CRED and 83.24% on SEED. Under the intra-subject setting, it reaches 63.24% on CRED and 85.37% on SEED. These results surpass existing methods and demonstrate the effectiveness of OFPCA. This study demonstrates that leveraging the inherent fuzziness of emotions enhances emotion recognition and introduces OFPCA as a novel fuzzy learning-based framework for multi-view EEG learning.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Efficiency in CNN-Based Respiratory Sound Analysis through Temporal Self-Attention and Frequency Band Selection. 通过时间自注意和频带选择提高cnn呼吸声分析的效率。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11251767

Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho

{"title":"Enhancing Efficiency in CNN-Based Respiratory Sound Analysis through Temporal Self-Attention and Frequency Band Selection.","authors":"Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho","doi":"10.1109/EMBC58623.2025.11251767","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251767","url":null,"abstract":"Automating lung sound classification necessitates capturing both local spectral patterns and long-range temporal dependencies, a challenge for traditional models. While Convolutional Neural Networks (CNNs) excel at local feature extraction, they struggle with modeling long-range dependencies. Transformer-based models mitigate this limitation but incur high computational costs, rendering them impractical for real-time use in resource-constrained settings. To address these challenges, this paper proposes a lightweight CNN-based Temporal Self-Attention (CNN-TSA) network integrated with a Frequency Band Selection (FBS) strategy. The CNN-TSA efficiently captures temporal dependencies, while FBS eliminates irrelevant frequency bands, enhancing noise resilience and generalization. Experiments on the SPRSound 2022 dataset show that the proposed approach reduces computational costs by 50% without compromising performance, achieving state-of-the-art results. This efficient framework facilitates reliable real-time lung sound analysis, making it a promising solution for healthcare applications in resource-limited environments.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Honeycomb Electrode Arrangement Improves Stability of Sputtered Iridium Oxide Electrodes. 蜂窝电极结构提高了氧化铱溅射电极的稳定性。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11251625

Lukas Matter, Brad Raos, Brittany Hazelgrove, Bruce Harland, Salvador Lopez, Darren Svirskis, Maria Asplund

{"title":"Honeycomb Electrode Arrangement Improves Stability of Sputtered Iridium Oxide Electrodes.","authors":"Lukas Matter, Brad Raos, Brittany Hazelgrove, Bruce Harland, Salvador Lopez, Darren Svirskis, Maria Asplund","doi":"10.1109/EMBC58623.2025.11251625","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251625","url":null,"abstract":"The stability of thin-film electrodes is critical for the long-term performance of chronic neural implants, specifically, mechanical integrity must be retained. This study systematically evaluated the stability of sputtered iridium oxide film (SIROF) electrodes in a distributed honeycomb arrangement compared to rectangular electrodes. Both electrodes were fabricated to cover equivalent areas of a polyimide (PI) substrate. Electrochemical analysis indicated that both electrodes have comparable electrochemical surface area (ECSA), likely even when coated with PEDOT/PSS, provided the charge density cutoff during electrodeposition is based on ECSA rather than the physical electrode area. The honeycomb electrode demonstrated increased structural integrity in tape tests and in cyclic voltammetry (CV) designed to intentionally induce delamination through gas evolution in water-splitting reactions. These findings suggest that distributing small, interconnected electrodes instead of a large continuous electrode increases stability, making it promising for neural implant applications.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extraction of Risk Markers from ECG in Patients with Hypertrophic Cardiomyopathy. 肥厚性心肌病患者心电图危险标志物的提取。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11254105

Marion Taconne, Valentina D A Corino, Alex Melot, Adrien Al Wazzan, Erwan Donal, Pietro Cerveri, Luca Mainardi

{"title":"Extraction of Risk Markers from ECG in Patients with Hypertrophic Cardiomyopathy.","authors":"Marion Taconne, Valentina D A Corino, Alex Melot, Adrien Al Wazzan, Erwan Donal, Pietro Cerveri, Luca Mainardi","doi":"10.1109/EMBC58623.2025.11254105","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254105","url":null,"abstract":"Hypertrophic cardiomyopathy (HCM) represents one of the leading causes of sudden cardiac death (SCD), particularly in the young population, with a risk of approximately 1% per year. So far, no reliable electrocardiogram (ECG) biomarkers have been presented for risk assessment, but ECG in HCM patients are often abnormal due to structural and electrical abnormalities. This study aimed to extract morphological ECG biomarkers to differentiate HCM patients based on their arrhythmic risk levels (15 HCM patients with arrhythmic events vs. 40 HCM control). We extracted ECG features including width, amplitudes, slopes between fiducial points, Hermite transform coefficients, and variational mode decomposition features. Following feature selection using combined metrics, the study population was divided into two groups for each ECG biomarker, with the median value serving as the cutoff point to distinguish between the groups. QRS and T waverelated features effectively separated patients into high and low arrhythmic risk categories. Notably, univariate Cox regression analysis showed that patients having more local QRS optima or highest percentage of negative QRS present the highest risk (p< 0.01 and p< 0.05 respectively). In conclusion, we proposed automatic ECG extracted features that can be used to stratify the risk for arrhythmic events in HCM patients.Clinical Relevance-This study provides novel insights into ECG-based risk stratification for HCM patients, offering potential tools for early identification of individuals at higher risk of cardiac events.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of kinematic similarity between non-assisted and assisted walking with a hip exoskeleton using a vector coding technique. 利用向量编码技术评估非辅助和辅助髋关节外骨骼行走的运动学相似性。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11253366

Chihyeong Lee, Hyeonwoo Kim, Chae Lynne Kim, Jooeun Ahn, Keewon Kim, Yujin Kwon

{"title":"Evaluation of kinematic similarity between non-assisted and assisted walking with a hip exoskeleton using a vector coding technique.","authors":"Chihyeong Lee, Hyeonwoo Kim, Chae Lynne Kim, Jooeun Ahn, Keewon Kim, Yujin Kwon","doi":"10.1109/EMBC58623.2025.11253366","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253366","url":null,"abstract":"Flexible wearable walking-assist robots, unlike rigid rehabilitation robots, offer lightweight designs and ease of wear. This study focused on quantifying differences in joint coordination patterns between robotic and human motion during gait. Eleven healthy participants completed 2-min treadmill walking trials in three conditions: normal walking (None), walking with the robot worn but inactive (Off), and walking with the robot worn and active (On). Using a vector coding technique, coupling angles and their variability were analyzed across four gait phases. An increase in the coupling angle was detected in the mid stance and late stance phases in the On condition, which was driven by changes in the knee joint angles. Coupling angle variability was significantly reduced in the terminal swing phase in the On condition compared to None, suggesting enhanced consistency in the movement. These findings suggest that a vector coding technique can detect differences in hip-knee joint coordination patterns in the mid and late stance phases between non-assisted and assisted walking with a hip assist robot and that coupling angle can be used as a measure for evaluating kinematics of assisted walking.Clinical Relevance- A vector coding technique can be used to evaluate the effect of walking assist robot on movement patterns for a better applicability in daily life.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracting Preserved Neural Latent Dynamics Across Tasks using Convolutional Transformer-based Variational Autoendecoder. 基于卷积变换的变分自编解码器提取任务间保留的神经潜在动态。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11251780

Zhiwei Song, Shenghui Wu, Taiyan Zhou, Yiwen Wang

{"title":"Extracting Preserved Neural Latent Dynamics Across Tasks using Convolutional Transformer-based Variational Autoendecoder.","authors":"Zhiwei Song, Shenghui Wu, Taiyan Zhou, Yiwen Wang","doi":"10.1109/EMBC58623.2025.11251780","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251780","url":null,"abstract":"Understanding how neural systems drive behavior is a fundamental goal in neuroscience. Numerous studies have demonstrated that the activity of large neural populations is often governed by low-dimensional neural dynamics. While much of the current research has focused on extracting informative and interpretable latent dynamics from individual motor tasks, it remains unclear whether these dynamics are preserved across different motor tasks. This question is particularly critical, as prior experience with a related task can facilitate faster learning in a new task. In this paper, we propose a Convolutional Transformer-based Variational Autoencoder (Conformer-VAE) to extract preserved neural latent dynamics across tasks by leveraging the rich spatiotemporal patterns in neural activity. We validate our approach using neural recordings from a rat, which first performed a one-lever pressing task (old task) and subsequently a two-lever discrimination task (new task). By projecting the inferred latent dynamics from both tasks onto a common 2D PCA plane, our results demonstrate that Conformer-VAE effectively captures preserved neural dynamics across tasks, outperforming baseline methods. Moreover, these preserved dynamics enable faster decoder training for the new task by transferring the neural-to-movement mapping learned from the old task. This capability facilitates seamless real-time task switching, offering promising applications for brain-machine interface systems.Clinical Relevance-This work facilitates faster adaptation in brain-machine interfaces by preserving neural dynamics across tasks, offering potential benefits for neuroprosthetics and motor rehabilitation in patients with motor impairments.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ex Vivo Porcine Liver Validation of Dixon Fat Fraction-Based Electrical Properties Models at 3T. 基于Dixon脂肪分数的离体猪肝脏3T电特性模型验证

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11253077

Kecheng Yuan, Yinhao Ren, Qingyun Liu, Guanfu Li, Bensheng Qiu, Jijun Han

{"title":"Ex Vivo Porcine Liver Validation of Dixon Fat Fraction-Based Electrical Properties Models at 3T.","authors":"Kecheng Yuan, Yinhao Ren, Qingyun Liu, Guanfu Li, Bensheng Qiu, Jijun Han","doi":"10.1109/EMBC58623.2025.11253077","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253077","url":null,"abstract":"Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive imaging technique for mapping tissue electrical properties (EPs), offering significant potential for disease diagnosis and ablation therapy. Previously proposed Dixon-based techniques for liver electrical properties (EPs) were established on literature values and lack exploration of individual differences. We developed a Dixon Fat Fraction Electrical Properties (FF-EPs) model and successfully reconstructed EPs from phantoms using this model. Differences in measurements between the FF-EPs technique and the open-end coaxial probe (OECP) method were investigated by ex vivo porcine livers experiments. The absolute error of the FF-EPs reconstructed permittivity is 2.43, and the absolute error of the conductivity is 0.125 S/m. This study validates the effectiveness of FF-EPs and holds promise for clinical application in acquiring patient liver EPs, thereby aiding in disease diagnosis and guiding ablation therapy.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble Guided Fine-Tuning Pre-Trained Models for Kinase Inhibitor Design. 集合引导微调预训练模型激酶抑制剂设计。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11253376

Tze Shin Chen, Jhih Wei Chu, Jinn Moon Yang

{"title":"Ensemble Guided Fine-Tuning Pre-Trained Models for Kinase Inhibitor Design.","authors":"Tze Shin Chen, Jhih Wei Chu, Jinn Moon Yang","doi":"10.1109/EMBC58623.2025.11253376","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253376","url":null,"abstract":"This study introduces an innovative framework, Ensemble Guide Fine-tuning (EGFit), for Pre-Trained Models, designed to address the challenges of data scarcity in kinase targeted drug discovery. Protein kinases play a pivotal role in cancer, immune diseases, and other complex disorders, making them a critical drug target. Despite over 100,000 recorded kinase inhibitors, only about 75 small-molecule kinase drugs have received FDA approval, underscoring the difficulty of developing kinase drugs. EGFit combines pre-trained large language models, with advanced machine learning techniques, including random forest, support vector machine, multilayer perceptrons, and logistic regression, to iteratively evaluate and refine generated compounds. Under limited data conditions, the framework efficiently explores a vast chemical space, producing biologically relevant and structurally diverse kinase inhibitors. Experimental validation on four kinases (EGFR, MET, PIM1, and CDK5) demonstrates significant improvements in compound similarity to known inhibitors while maintaining compliance with drug-likeness criteria. The iterative feedback mechanism further ensures chemical novelty and biological significance, showcasing the potential of EGFit to optimize compound generation for kinase-specific applications. This framework offers a scalable and effective solution to the challenges of kinase drug discovery, accelerating the development of novel therapeutics and paving the way for broader applications in future studies.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of Upper Limb Dynamic Interaction Force Based on Multimodal Information. 基于多模态信息的上肢动力交互力估计。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2025-07-01 DOI: 10.1109/EMBC58623.2025.11252887

Yalun Gu, Daohui Zhang, Dezhen Xiong, Xingang Zhao

引用次数: 0