International Conference on Biomedical and Intelligent Systems最新文献

LncRNA LINC00665 affected gastric cancer through Mir-9-5p according to CeRNA network analysis 根据CeRNA网络分析，LncRNA LINC00665通过Mir-9-5p影响胃癌

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687538

Dayuan Chen, Fan Chen, P. Guo, Hongbo Chen

{"title":"LncRNA LINC00665 affected gastric cancer through Mir-9-5p according to CeRNA network analysis","authors":"Dayuan Chen, Fan Chen, P. Guo, Hongbo Chen","doi":"10.1117/12.2687538","DOIUrl":"https://doi.org/10.1117/12.2687538","url":null,"abstract":"Gastric cancer (GC) is one of the most common cancers in the world. Although the incidence and mortality rates of GC have declined globally in the past decades, its prognosis is still poor. Moreover, the exact mechanism of GC has not been thoroughly studied. This study aimed to identify central genes to improve the prognostic prediction of GC and construct a regulatory network of messenger RNA (mRNA), microRNA (miRNA), and long noncoding RNA (lncRNA). Six genes (COL10A1, CTHRC1, FAP, FNDC1, INHBA, and SULF1) were found using the information in the GEO database. The expression of these genes differs between patients with GC and nontumor groups, and such difference may affect the survival rate of patients with GC. A ceRNA network consisting of mRNA (CTHRC1, FNDC1, and INHBA), miRNA (mir-9-5p), and lncRNA (LINC00665) was constructed to reverse predict the target genes related to GC prognosis. According to the ceRNA theory, LINC00665 could bind with mir-9-5p in a competitive manner, increasing the transcription of CTHRC1, FNDC1, and INHBA regulated by mir-9-5p. However, this study has many limitations. First, age, sex, tumor stage, patient classification, and other factors were not considered when examining differentially expressed genes. Second, only FNDC1 expression in mRNA was shown.","PeriodicalId":217687,"journal":{"name":"International Conference on Biomedical and Intelligent Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121814543","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 review of the factors influencing the infusion accuracy of medical infusion pumps 影响医用输液泵输液精度的因素综述

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687441

Xue Cao, Miao Guo, Qingjuan Feng, Yang Wang, J. Gao

引用次数: 0

Research on QRS waveform detection based on difference absolute value extreme 基于差分绝对值极值的QRS波形检测研究

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687529

shun yu, Ping Zhang, Xun Zhao

引用次数: 0

A quantitative analysis of training effects for the touch screen-based flashcard game attention training 基于触摸屏的抽认卡游戏注意力训练效果的定量分析

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687395

Yixing He, Xiyang Zhang, Shengjing Hu, Suogang Wang

引用次数: 0

Intelligent detection and classification of Alzheimer's disease based on machine learning 基于机器学习的阿尔茨海默病智能检测与分类

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687379

Jiaming Song, Zhenhao Wei, Jiayi Yi, Siyi Liu

引用次数: 0

MRMHNet: a new convolutional neural network approach for decoding electroencephalogram motor imagery signals MRMHNet:一种新的卷积神经网络方法来解码脑电图运动图像信号

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687404

Menghao Liu, Dong-Lun Wu, Xue-hua Tang, Zhiyong Zhou, Pengfei Zhao, Xiangmin Li, Xu Zhang

{"title":"MRMHNet: a new convolutional neural network approach for decoding electroencephalogram motor imagery signals","authors":"Menghao Liu, Dong-Lun Wu, Xue-hua Tang, Zhiyong Zhou, Pengfei Zhao, Xiangmin Li, Xu Zhang","doi":"10.1117/12.2687404","DOIUrl":"https://doi.org/10.1117/12.2687404","url":null,"abstract":"The decoding of electroencephalogram (EEG) signals plays an extremely important role in brain-computer interfaces (BCI). However, the processing of physiological signals, particularly the decoding of multi-channel EEG signals, still poses significant challenges. Past deep learning methods often relied on subject-dependent settings, which resulted in new users needing to perform complex calibration procedures before they could use BCI devices. Therefore, we proposed a novel end-to-end deep learning model, MRMHNet, for motor imagery (MI) classification. Firstly, we utilized a feature extraction block based on a Multi-Resolution convolutional neural network (MRCNN) to extract features in both frequency and spatial domains. Secondly, we utilized a block based on the Multi-Head Attention (MHA) to extract global temporal information of the features. Finally, we validated the classification performance of our method using OpenBMI datasets, and the results showed that our method achieved the highest accuracy in both subject-dependent and subject-independent settings. Specifically, in the subject-independent setting, our method achieved the highest accuracy and F1-score, with values of 73.74±13.35% and 73.33±14.87%, respectively. This indicates that our method has good classification performance and high practical value in the field of BCI.","PeriodicalId":217687,"journal":{"name":"International Conference on Biomedical and Intelligent Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131232279","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

Identification of natural products as dual sphingosine kinase-1 and programmed cell-death 1-inhibitors by virtual screening and molecular dynamics simulation 通过虚拟筛选和分子动力学模拟鉴定双重鞘氨醇激酶-1和程序性细胞死亡1抑制剂的天然产物

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687726

Jin Liu, Huilin Zhao, Lei He, Ri-lei Yu, C. Kang

引用次数: 0

Improvement of printed circuit-board-based digital microfluidic chip and its application in peptide screening 基于印刷电路板的数字微流控芯片的改进及其在肽筛选中的应用

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687405

Xing Wu, Dongbao Tang, Luxuan Liu, Zhaoyuan Jia, Yuyu Tan

引用次数: 0

Pulmonary nodule detection based on 3D multi-scale and semantic context heterogeneity 基于三维多尺度和语义上下文异质性的肺结节检测

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687875

Yunchang Zhang, Xiaoqing Luo

{"title":"Pulmonary nodule detection based on 3D multi-scale and semantic context heterogeneity","authors":"Yunchang Zhang, Xiaoqing Luo","doi":"10.1117/12.2687875","DOIUrl":"https://doi.org/10.1117/12.2687875","url":null,"abstract":"Automated lung nodule detection methods are challenged by the fact that pulmonary nodules often present multiple morphologies and scales and by the low differentiation of nodules from surrounding tissues. To address the above issues, we propose a pulmonary nodule detection method based on 3D multiscale and semantic context heterogeneity. The method uses dilated convolutions with different dilation rates to construct a Context Fusion Module (CFM) to extract pulmonary nodule multiscale fused features, thus enhancing the detection ability of non-significant and small nodules. The Channel Semantic Context Heterogeneity Model (CSHM) is proposed to address the problem of noise and redundant information in multi-scale fusion features, to suppress noise and redundant information from feature channel-level contextual relevance and redundancy, to enhance potential discriminative features, and to reduce the interference of useless information, so as to enhance the ability of nodule detection and localization. Experiments were conducted on a publicly available dataset (Luna Nodule Analysis 16, LUNA16) with a high sensitivity and an average sensitivity of 83.6% over eight false alarm numbers, which is better than the other method, validating the superiority of the proposed method.","PeriodicalId":217687,"journal":{"name":"International Conference on Biomedical and Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115822133","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

An efficient hybrid XGBLR-IMBODE model for heart disease prediction 一种用于心脏病预测的高效混合XGBLR-IMBODE模型

International Conference on Biomedical and Intelligent Systems Pub Date : 2023-08-28 DOI: 10.1117/12.2687815

Weijun Gao, Pengfei Fu, Zhenyu Wang

引用次数: 0