2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)最新文献_第3页

ConvE-Bio: Knowledge Graph Embedding for Biomedical Relation Prediction 生物医学关系预测的知识图嵌入

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061292

Xiaohan Qu, Yongming Cai

{"title":"ConvE-Bio: Knowledge Graph Embedding for Biomedical Relation Prediction","authors":"Xiaohan Qu, Yongming Cai","doi":"10.1109/ISBP57705.2023.10061292","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061292","url":null,"abstract":"Biomedical relation classification aims to automate the detection and classification of biomedical relationships, which has great advantages for various biomedical research and applications. With the development of machine learning, computational model-based approaches have been applied to biomedical relation classification and achieved state-of-the-art performance on some public datasets and shared tasks. Nevertheless, the existing models have some limitations in expressing features of large knowledge graphs. For example, the multilayer Knowledge Graph Embedding (KGE) network structure has fully connected layers and is prone to overfitting. Inspired by the multi-layer convolutional network model ConvE, this paper proposes a novel KGE model named ConvE-Bio for biomedical relation classification. The novel model performs well on the DDI (Drug-Drug Interaction), DTI (Drug-Target Interaction), and PPI (Protein-Protein Interaction) datasets, outperforming the classical baseline algorithms. Results show that ConvE-Bio can be used as a powerful tool in the field of biomedical relation classification for drug development, polypharmacy side-effect prediction and other research.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115794204","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

U-Net multi-modality glioma MRIs segmentation combined with attention U-Net多模态胶质瘤mri分割结合关注

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061312

Yixing Wang, Xiufen Ye

{"title":"U-Net multi-modality glioma MRIs segmentation combined with attention","authors":"Yixing Wang, Xiufen Ye","doi":"10.1109/ISBP57705.2023.10061312","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061312","url":null,"abstract":"Glioma, the most common primary intracranial tumor, is known as the “brain killer,” accounting for 27% of all central nervous system tumors and 80% of malignant tumors, and is one of the most difficult and refractory tumors to treat in neurosurgery. The development of medical imaging technology has simplified the diagnosis of the disease, and in order to avoid or reduce the errors of manual segmentation, deep learning based segmentation of glioma has become the hope of radiologists and clinicians. Accurate segmentation of gliomas is an important prerequisite for making glioma diagnosis, providing treatment plans and evaluating treatment outcomes. To effectively target the characteristics of multimodal glioma MRI and the shortcomings of CNNs-based, U-Net-based glioma segmentation methods, a method of 2D-CNNs segmentation results based on attention mechanism is proposed. In this study, the datasets of BraTS2018 and BraTS2019 were included and the segmentation results were evaluated using three metrics: Dice coefficient, positive predictive value, and sensitivity. The experimental results show that the proposed segmentation method can accurately segment gliomas.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122650299","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

A Deep Learning Method with Self-Attention Mechanism for Cross-Subject Sleep Stage Classification Based on EEG and EOG 基于脑电和眼电的跨主体睡眠阶段分类的自注意深度学习方法

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061318

Jianjun Huang, Jun Qu

引用次数: 0

Deep Learning-based Identification of DNA-N4 Methylcytosine Modification Sites 基于深度学习的DNA-N4甲基胞嘧啶修饰位点鉴定

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061304

Xiaolong Wu

引用次数: 0

AI Technology for Anti-Aging: an Overview 人工智能抗衰老技术综述

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061311

Aiquan Huang, Yingyu Huo, Yong Zhong, Wenyin Yang

引用次数: 0

10-Hz Repetitive Transcranial Magnetic Stimulation over the Frontal Eye Field Modulates Feature-Based Attention 10赫兹重复经颅磁刺激对前额眼场的调节基于特征的注意

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061302

Nianlin Li, Fuwu Yan, Lirong Yan, Yibo Wu, Biao Xiang

引用次数: 0