IEEE Journal of Biomedical and Health Informatics最新文献_第10页

mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks. mDARTS：针对成员推断攻击搜索基于 ML 的心电图分类器。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3481505

Eunbin Park, Youngjoo Lee

{"title":"mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks.","authors":"Eunbin Park, Youngjoo Lee","doi":"10.1109/JBHI.2024.3481505","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3481505","url":null,"abstract":"This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that balance high classification performance with robust privacy protection against membership inference attacks (MIA). We introduce a comprehensive approach that innovates in both machine learning efficacy and privacy preservation. Key contributions include the development of a privacy estimator to quantify and mitigate privacy leakage in neural network architectures used for ECG classification. Utilizing this privacy estimator, we propose mDARTS (searching MLbased ECG classifier against MIA), integrating MIA's attack loss into the architecture search process to identify architectures that are both accurate and resilient to MIA threats. Our method achieves significant improvements, with an ECG classification accuracy of 92.1% and a lower privacy score of 54.3%, indicating reduced potential for sensitive information leakage. Heuristic experiments refine architecture search parameters specifically for ECG classification, enhancing classifier performance and privacy scores by up to 3.0% and 1.0%, respectively. The framework's adaptability supports user customization, enabling the extraction of architectures that meet specific criteria such as optimal classification performance with minimal privacy risk. By focusing on the intersection of high-performance ECG classification and the mitigation of privacy risks associated with MIA, our study offers a pioneering solution addressing the limitations of previous approaches.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464111","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

Attention-guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI. 利用纵向 sMRI 通过病变特征选择进行早期阿尔茨海默病预测的注意力引导 3D CNN。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3482001

Jinwei Liu, Yashu Xu, Yi Liu, Huating Luo, Wenxiang Huang, Lizhong Yao

{"title":"Attention-guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.","authors":"Jinwei Liu, Yashu Xu, Yi Liu, Huating Luo, Wenxiang Huang, Lizhong Yao","doi":"10.1109/JBHI.2024.3482001","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3482001","url":null,"abstract":"Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic resonance imaging (sMRI) data from a single time point whereas neglecting the dynamic changes in brain structure over time. Current longitudinal studies inadequately explore disease evolution dynamics and are burdened by high computational complexity. This paper introduces a novel lightweight 3D convolutional neural network specifically designed to capture the evolution of brain diseases for modeling the progression of MCI. First, a longitudinal lesion feature selection strategy is proposed to extract core features from temporal data, facilitating the detection of subtle differences in brain structure between two time points. Next, to refine the model for a more concentrated emphasis on lesion features, a disease trend attention mechanism is introduced to learn the dependencies between overall disease trends and local variation features. Finally, disease prediction visualization techniques are employed to improve the interpretability of the final predictions. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of area under the curve (AUC), accuracy, specificity, precision, and F1 score. This study confirms the efficacy of our early diagnostic method, utilizing only two follow-up sMRI scans to predict the disease status of MCI patients 24 months later with an AUC of 79.03%.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464060","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

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction. 时空神经网络的可解释多分支架构及其在癫痫发作预测中的应用

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3481005

Baolian Shan, Haiqing Yu, Yongzhi Huang, Minpeng Xu, Dong Ming

{"title":"Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.","authors":"Baolian Shan, Haiqing Yu, Yongzhi Huang, Minpeng Xu, Dong Ming","doi":"10.1109/JBHI.2024.3481005","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3481005","url":null,"abstract":"Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different frequencies in different brain regions. Therefore, it may be underperforming and uninterpretable for the CNNs without capturing complex spectral properties sufficiently. This study proposed a novel interpretable multi-branch architecture for spatiotemporal CNNs, namely MultiSincNet. On the one hand, the MultiSincNet could directly show the frequency boundaries using the interpretable sinc-convolution layers. On the other hand, it could extract and integrate multiple spatiotemporal features across varying spectral resolutions using parallel branches. Moreover, we also constructed a post-hoc explanation technique for multi-branch CNNs, using the first-order Taylor expansion and chain rule based on the multivariate composite function, which demonstrates the crucial spatiotemporal features learned by the proposed multi-branch spatiotemporal CNN. When combined with the optimal MultiSincNet, ShallowConvNet, DeepConvNet, and EEGWaveNet had significantly improved the subject-specific performance on most metrics. Specifically, the optimal MultiSincNet significantly increased the average accuracy, sensitivity, specificity, binary F1-score, weighted F1-score, and AUC of EEGWaveNet by about 7%, 8%, 7%, 8%, 7%, and 7%, respectively. Besides, the visualization results showed that the optimal model mainly extracts the spectral energy difference from the high gamma band focalized to specific spatial areas as the dominant spatiotemporal EEG feature.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464109","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

Agnostic-Specific Modality Learning for Cancer Survival Prediction from Multiple Data. 从多种数据中预测癌症生存期的不可知特定模式学习。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3481310

Honglei Liu, Yi Shi, Ying Xu, Ao Li, Minghui Wang

{"title":"Agnostic-Specific Modality Learning for Cancer Survival Prediction from Multiple Data.","authors":"Honglei Liu, Yi Shi, Ying Xu, Ao Li, Minghui Wang","doi":"10.1109/JBHI.2024.3481310","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3481310","url":null,"abstract":"Cancer is a pressing public health problem and one of the main causes of mortality worldwide. The development of advanced computational methods for predicting cancer survival is pivotal in aiding clinicians to formulate effective treatment strategies and improve patient quality of life. Recent advances in survival prediction methods show that integrating diverse information from various cancer-related data, such as pathological images and genomics, is crucial for improving prediction accuracy. Despite promising results of existing approaches, there are great challenges of modality gap and semantic redundancy presented in multiple cancer data, which could hinder the comprehensive integration and pose substantial obstacles to further enhancing cancer survival prediction. In this study, we propose a novel agnostic-specific modality learning (ASML) framework for accurate cancer survival prediction. To bridge the modality gap and provide a comprehensive view of distinct data modalities, we employ an agnostic-specific learning strategy to learn the commonality across modalities and the uniqueness of each modality. Moreover, a cross-modal fusion network is exerted to integrate multimodal information by modeling modality correlations and diminish semantic redundancy in a divide-and-conquer manner. Extensive experiment results on three TCGA datasets demonstrate that ASML reaches better performance than other existing cancer survival prediction methods for multiple data.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464059","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

Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS. 利用基于进化算法的 NAS 在弥漫性肺病图像分类中进行特征分离

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3481012

Qing Zhang, Dan Shao, Lin Lin, Guoliang Gong, Rui Xu, Shoji Kido, HongWei Cui

{"title":"Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS.","authors":"Qing Zhang, Dan Shao, Lin Lin, Guoliang Gong, Rui Xu, Shoji Kido, HongWei Cui","doi":"10.1109/JBHI.2024.3481012","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3481012","url":null,"abstract":"In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered \"black boxes,\" making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0.56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464106","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

Joint Energy-based Model for Semi-supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch. 基于联合能量的半监督呼吸声分类模型：一种对分布不匹配不敏感的方法

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3480999

Wenjie Song, Jiqing Han, Shiwen Deng, Tieran Zheng, Guibin Zheng, Yongjun He

{"title":"Joint Energy-based Model for Semi-supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch.","authors":"Wenjie Song, Jiqing Han, Shiwen Deng, Tieran Zheng, Guibin Zheng, Yongjun He","doi":"10.1109/JBHI.2024.3480999","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3480999","url":null,"abstract":"Semi-supervised learning effectively mitigates the lack of labeled data by introducing extensive unlabeled data. Despite achieving success in respiratory sound classification, in practice, it usually takes years to acquire a sufficiently sizeable unlabeled set, which consequently results in an extension of the research timeline. Considering that there are also respiratory sounds available in other related tasks, like breath phase detection and COVID-19 detection, it might be an alternative manner to treat these external samples as unlabeled data for respiratory sound classification. However, since these external samples are collected in different scenarios via different devices, there inevitably exists a distribution mismatch between the labeled and external unlabeled data. For existing methods, they usually assume that the labeled and unlabeled data follow the same data distribution. Therefore, they cannot benefit from external samples. To utilize external unlabeled data, we propose a semi-supervised method based on Joint Energy-based Model (JEM) in this paper. During training, the method attempts to use only the essential semantic components within the samples to model the data distribution. When non-semantic components like recording environments and devices vary, as these non-semantic components have a small impact on the model training, a relatively accurate distribution estimation is obtained. Therefore, the method exhibits insensitivity to the distribution mismatch, enabling the model to leverage external unlabeled data to mitigate the lack of labeled data. Taking ICBHI 2017 as the labeled set, HF_Lung_V1 and COVID-19 Sounds as the external unlabeled sets, the proposed method exceeds the baseline by 12.86.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464110","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

Aceso-DSAL: Discovering Clinical Evidences from Medical Literature Based on Distant Supervision and Active Learning. Aceso-DSAL：基于远程监督和主动学习从医学文献中发现临床证据

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3480998

Xiang Zhang, Jiaxin Hu, Qian Lu, Lu Niu, Xinqi Wang

{"title":"Aceso-DSAL: Discovering Clinical Evidences from Medical Literature Based on Distant Supervision and Active Learning.","authors":"Xiang Zhang, Jiaxin Hu, Qian Lu, Lu Niu, Xinqi Wang","doi":"10.1109/JBHI.2024.3480998","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3480998","url":null,"abstract":"Automatic extraction of valuable, structured evidence from the exponentially growing clinical trial literature can help physicians practice evidence-based medicine quickly and accurately. However, current research on evidence extraction has been limited by the lack of generalization ability on various clinical topics and the high cost of manual annotation. In this work, we address these challenges by constructing a PICO-based evidence dataset PICO-DS, covering five clinical topics. This dataset was automatically labeled by a distant supervision based on our proposed textual similarity algorithm called ROUGE-Hybrid. We then present an Aceso-DSAL model, an extension of our previous supervised evidence extraction model - Aceso. In Aceso-DSAL, distantly-labelled and multi-topic PICO-DS was exploited as training corpus, which greatly enhances the generalization of the extraction model. To mitigate the influence of noise unavoidably-introduced in distant supervision, we employ TextCNN and MW-Net models and a paradigm of active learning to weigh the value of each sample. We evaluate the effectiveness of our model on the PICO-DS dataset and find that it outperforms state-of-the-art studies in identifying evidential sentences.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464058","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

Fall Detection Method based on a Human Electrostatic Field and VMD-ECANet Architecture. 基于人体静电场和 VMD-ECANet 架构的跌倒检测方法。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3481237

Xi Chen, Jiaao Yan, Sichao Qin, Pengfei Li, Shuangqian Ning, Yuting Liu

{"title":"Fall Detection Method based on a Human Electrostatic Field and VMD-ECANet Architecture.","authors":"Xi Chen, Jiaao Yan, Sichao Qin, Pengfei Li, Shuangqian Ning, Yuting Liu","doi":"10.1109/JBHI.2024.3481237","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3481237","url":null,"abstract":"Falls are one of the most serious health risks faced by older adults worldwide, and they can have a significant impact on their physical and mental well-being as well as their quality of life. Detecting falls promptly and accurately and providing assistance can effectively reduce the harm caused by falls to older adults. This paper proposed a noncontact fall detection method based on the human electrostatic field and a VMD-ECANet framework. An electrostatic measurement system was used to measure the electrostatic signals of four types of falling postures and five types of daily actions. The signals were randomly divided in proportion and by individuals to construct a training set and test set. A fall detection model based on the VMD-ECA network was proposed that decomposes electrostatic signals into modal component signals using the variational mode decomposition (VMD) technique. These signals were then fed into a multichannel convolutional neural network for feature extraction. Information fusion was achieved through the efficient channel attention network (ECANet) module. Finally, the extracted features were input into a classifier to obtain the output results. The constructed model achieved an accuracy of 96.44%. The proposed fall detection solution has several advantages, including being noncontact, cost-effective, and privacy friendly. It is suitable for detecting indoor falls by older individuals living alone and helps to reduce the harm caused by falls.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464064","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

TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning. TKR-FSOD：利用拓扑知识推理的胎儿解剖结构少拍检测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-14 DOI: 10.1109/JBHI.2024.3480197

Xi Li, Ying Tan, Bochen Liang, Bin Pu, Jiewen Yang, Lei Zhao, Yanqing Kong, Lixian Yang, Rentie Zhang, Hao Li, Shengli Li

{"title":"TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning.","authors":"Xi Li, Ying Tan, Bochen Liang, Bin Pu, Jiewen Yang, Lei Zhao, Yanqing Kong, Lixian Yang, Rentie Zhang, Hao Li, Shengli Li","doi":"10.1109/JBHI.2024.3480197","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3480197","url":null,"abstract":"Fetal multi-anatomical structure detection in Ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in making more accurate diagnoses, widely used in structure evaluation. Recently, deep learning methods have shown superior performance in detecting various anatomical structures in ultrasound images but still have the potential for performance improvement in categories where it is difficult to obtain samples, such as rare diseases. Few-shot learning has attracted a lot of attention in medical image analysis due to its ability to solve the problem of data scarcity. However, existing few-shot learning research in medical image analysis focuses on classification and segmentation, and the research on object detection has been neglected. In this paper, we propose a novel fetal anatomical structure fewshot detection method in ultrasound images, TKR-FSOD, which learns topological knowledge through a Topological Knowledge Reasoning Module to help the model reason about and detect anatomical structures. Furthermore, we propose a Discriminate Ability Enhanced Feature Learning Module that extracts abundant discriminative features to enhance the model's discriminative ability. Experimental results demonstrate that our method outperforms the state-of-the-art baseline methods, exceeding the second best method with a maximum margin of 4.8% on 5-shot of split 1 under 4CC. The code is publicly available at: https://github.com/lixi92/TKR-FSOD.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464116","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

Self-Supervised Molecular Representation Learning With Topology and Geometry. 利用拓扑学和几何学进行自我监督分子表征学习

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-14 DOI: 10.1109/JBHI.2024.3479194

Xuan Zang, Junjie Zhang, Buzhou Tang

{"title":"Self-Supervised Molecular Representation Learning With Topology and Geometry.","authors":"Xuan Zang, Junjie Zhang, Buzhou Tang","doi":"10.1109/JBHI.2024.3479194","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3479194","url":null,"abstract":"Molecular representation learning is of great importance for drug molecular analysis. The development in molecular representation learning has demonstrated great promise through self-supervised pre-training strategy to overcome the scarcity of labeled molecular property data. Recent studies concentrate on pre-training molecular representation encoders by integrating both 2D topological and 3D geometric structures. However, existing methods rely on molecule-level or atom-level alignment for different views, while overlooking hierarchical self-supervised learning to capture both inter-molecule and intra-molecule correlation. Additionally, most methods employ 2D or 3D encoders to individually extract molecular characteristics locally or globally for molecular property prediction. The potential for effectively fusing these two molecular representations remains to be explored. In this work, we propose a Multi-View Molecular Representation Learning method (MVMRL) for molecular property prediction. First, hierarchical pre-training pretext tasks are designed, including fine-grained atom-level tasks for 2D molecular graphs as well as coarse-grained molecule-level tasks for 3D molecular graphs to provide complementary information to each other. Subsequently, a motif-level fusion pattern of multi-view molecular representations is presented during fine-tuning to enhance the performance of molecular property prediction. We evaluate the effectiveness of the proposed MVMRL by comparing with state-of-the-art baselines on molecular property prediction tasks, and the experimental results demonstrate the superiority of MVMRL.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464115","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