Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061292
Xiaohan Qu, Yongming Cai
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061312
Yixing Wang, Xiufen Ye
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061318
Jianjun Huang, Jun Qu
Brain-computer interface (BCI) systems based on electroencephalography (EEG) and electrooculogram (EOG) were shown to be able to be used for automatic sleep stage classification since these two signals contain many sleep characteristics. However, EEG signal characteristics vary greatly among individuals, and manual classification is time-consuming and subjective. This paper proposes a deep learning method using a self-attention mechanism to achieve cross-subject sleep stage classification based on EEG and EOG. The method mainly consists of three parts. First, a traditional convolutional neural network is used to perform preliminary feature extraction on the information of the two channels. Then use Long Short-Term Memory (LSTM) to find the features in time series. Finally, the self-attention mechanism is used to find more mission-critical information from the high-dimensional feature information. We performed 25-fold cross-validation experiments and showed that the model achieved an average accuracy of 82.4% and 80.4 of the macro-averaging F1 Score(MF1).
{"title":"A Deep Learning Method with Self-Attention Mechanism for Cross-Subject Sleep Stage Classification Based on EEG and EOG","authors":"Jianjun Huang, Jun Qu","doi":"10.1109/ISBP57705.2023.10061318","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061318","url":null,"abstract":"Brain-computer interface (BCI) systems based on electroencephalography (EEG) and electrooculogram (EOG) were shown to be able to be used for automatic sleep stage classification since these two signals contain many sleep characteristics. However, EEG signal characteristics vary greatly among individuals, and manual classification is time-consuming and subjective. This paper proposes a deep learning method using a self-attention mechanism to achieve cross-subject sleep stage classification based on EEG and EOG. The method mainly consists of three parts. First, a traditional convolutional neural network is used to perform preliminary feature extraction on the information of the two channels. Then use Long Short-Term Memory (LSTM) to find the features in time series. Finally, the self-attention mechanism is used to find more mission-critical information from the high-dimensional feature information. We performed 25-fold cross-validation experiments and showed that the model achieved an average accuracy of 82.4% and 80.4 of the macro-averaging F1 Score(MF1).","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119734","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}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061304
Xiaolong Wu
DNA modification is closely related to the expression genetics of many organisms, therefore, the prediction of DNA modification sites is particularly important. In this paper, we use deep learning techniques to identify and predict DNA N4-methylcytosine modification sites, and the main work is as follows. Feature encoding using k-spacer nucleic acids to encode a 41 bp long DNA sequence as a (41×9) dimensional vector. Recognition prediction based on multi-headed attention mechanism and GRU neural network. Firstly, the encoded data are extracted and downscaled; secondly, the importance distribution of 4mc loci and each nucleotide in the sequence are further extracted adaptively using the multi-headed attention mechanism; then the GRU network is used to capture the long dependencies in the whole importance distribution; finally, a new prediction model of 4mc loci is constructed using two fully connected layers, and its recognition accuracy is significantly improved compared with other basic machine learning models. The recognition accuracy is improved compared with other basic machine learning models.
{"title":"Deep Learning-based Identification of DNA-N4 Methylcytosine Modification Sites","authors":"Xiaolong Wu","doi":"10.1109/ISBP57705.2023.10061304","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061304","url":null,"abstract":"DNA modification is closely related to the expression genetics of many organisms, therefore, the prediction of DNA modification sites is particularly important. In this paper, we use deep learning techniques to identify and predict DNA N4-methylcytosine modification sites, and the main work is as follows. Feature encoding using k-spacer nucleic acids to encode a 41 bp long DNA sequence as a (41×9) dimensional vector. Recognition prediction based on multi-headed attention mechanism and GRU neural network. Firstly, the encoded data are extracted and downscaled; secondly, the importance distribution of 4mc loci and each nucleotide in the sequence are further extracted adaptively using the multi-headed attention mechanism; then the GRU network is used to capture the long dependencies in the whole importance distribution; finally, a new prediction model of 4mc loci is constructed using two fully connected layers, and its recognition accuracy is significantly improved compared with other basic machine learning models. The recognition accuracy is improved compared with other basic machine learning models.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128579382","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}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061311
Aiquan Huang, Yingyu Huo, Yong Zhong, Wenyin Yang
With the accelerated aging of the global population, the research of anti-aging technology and its application has gradually become one of the hot issues in the biomedical field. In recent years, Artificial Intelligence technologies represented by machine learning, deep learning and cognitive computing, have provided unprecedented methods and tools for biomedical research, and brought breakthroughs to anti-aging, a comprehensive and cutting-edge research topic. This paper first discusses the current problems and challenges that need to be solved in the application of AI technology in anti-aging; then summarizes the current status of AI data research on basic anti-aging applications, analyzes and discusses the research and progress of AI technology in the frontier application areas such as 3D reconstruction of aging structures, aging biomarkers and anti-aging drug development; and finally provides an outlook on the future development trends.
{"title":"AI Technology for Anti-Aging: an Overview","authors":"Aiquan Huang, Yingyu Huo, Yong Zhong, Wenyin Yang","doi":"10.1109/ISBP57705.2023.10061311","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061311","url":null,"abstract":"With the accelerated aging of the global population, the research of anti-aging technology and its application has gradually become one of the hot issues in the biomedical field. In recent years, Artificial Intelligence technologies represented by machine learning, deep learning and cognitive computing, have provided unprecedented methods and tools for biomedical research, and brought breakthroughs to anti-aging, a comprehensive and cutting-edge research topic. This paper first discusses the current problems and challenges that need to be solved in the application of AI technology in anti-aging; then summarizes the current status of AI data research on basic anti-aging applications, analyzes and discusses the research and progress of AI technology in the frontier application areas such as 3D reconstruction of aging structures, aging biomarkers and anti-aging drug development; and finally provides an outlook on the future development trends.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114766707","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}
The frontal eye field (FEF) is an important brain area related to visual feature-based attention (FBA). In this study, we applied 10-Hz repetitive transcranial magnetic stimulation (rTMS) to the right FEF (rFEF), designed an improved version of attention network test (ANT) with two attributes (direction and color), to explore the relationship between rFEF and the attentional network (including alerting, orienting and executive control network) and between rFEF and visual FBA. 24 healthy subjects completed the improved ANT after stimulation. The sham stimulation experiment was set as the control group. The experimental results show that the stimuli applied to rFEF can not significantly affect the attention subnetwork. However, rFEF, as a part of the frontoparietal network, affects the relevant connections of the frontoparietal network after a short local stimulation, thereby significantly reducing the reaction time of the subjects. In addition, rFEF is closely related to attribute selection in visual FBA tasks, which has been confirmed.
{"title":"10-Hz Repetitive Transcranial Magnetic Stimulation over the Frontal Eye Field Modulates Feature-Based Attention","authors":"Nianlin Li, Fuwu Yan, Lirong Yan, Yibo Wu, Biao Xiang","doi":"10.1109/ISBP57705.2023.10061302","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061302","url":null,"abstract":"The frontal eye field (FEF) is an important brain area related to visual feature-based attention (FBA). In this study, we applied 10-Hz repetitive transcranial magnetic stimulation (rTMS) to the right FEF (rFEF), designed an improved version of attention network test (ANT) with two attributes (direction and color), to explore the relationship between rFEF and the attentional network (including alerting, orienting and executive control network) and between rFEF and visual FBA. 24 healthy subjects completed the improved ANT after stimulation. The sham stimulation experiment was set as the control group. The experimental results show that the stimuli applied to rFEF can not significantly affect the attention subnetwork. However, rFEF, as a part of the frontoparietal network, affects the relevant connections of the frontoparietal network after a short local stimulation, thereby significantly reducing the reaction time of the subjects. In addition, rFEF is closely related to attribute selection in visual FBA tasks, which has been confirmed.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128884308","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}
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