The Fifth International Conference on Biological Information and Biomedical Engineering最新文献_第2页

Identification of Key Gene Modules and Hub Genes of Hypertension Based on WGCNA Algorithm 基于WGCNA算法的高血压关键基因模块和枢纽基因鉴定

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469701

Zongjin Li, Changxin Song, Zeyu Jia, Dong Chen, Yan Liang

{"title":"Identification of Key Gene Modules and Hub Genes of Hypertension Based on WGCNA Algorithm","authors":"Zongjin Li, Changxin Song, Zeyu Jia, Dong Chen, Yan Liang","doi":"10.1145/3469678.3469701","DOIUrl":"https://doi.org/10.1145/3469678.3469701","url":null,"abstract":"Background: Hypertension is a chronic disease with high morbidity and high mortality in the world. Its pathogenesis is complicated and its molecular mechanism has not been fully elucidated, which seriously threatens human life and health. The purpose of this paper was to the molecular study of hypertension, explore candidate biomarkers affecting the occurrence of hypertension from the perspective of weighted network, and provide the theoretical and practical basis for the prevention and treatment of hypertension. Materials and methods: The hypertension gene expression dataset of GSE75360 were downloaded from the Gene Expression Omnibus (GEO). The “limma” package of R was utilized to screen the differentially expressed genes (DEGs) between the sample group with and without high blood pressure. Next, Weight Gene co-expression Network Analysis (WGCNA) algorithm was used to establish a co-expression network of the DEGs and to detect hypertension-related gene modules. And DAVID was utilized to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we proposed the hierarchical fusion method to screen hub genes. Results: We identified 2 key gene modules that were significantly associated with hypertension, named Mlightcyan and Mgreenyellow. In addition, 18 hub genes (RPS28, LOC730288/RPS28P6, LOC645968/ RPS3AP25, LOC727826/RPS11P5, RPL21, LOC653079/ RPL36P14, LOC441743/RPL23AP5, LOC651453/RPL36P14, LPPR2, NSMCE4A, FKBP1A, RAB5C, MAN2B1, FURIN, TBXAS1, RPS6KA4, PARN, LOC642489/FKBP1C) relating to hypertension were identified form the two key gene modules. Conclusions: In this study, we identified two key gene modules and 18 hub genes, which were associated with the mechanisms of hypertension. These findings will provide references that improve the understanding of the pathogenesis of hypertension. The hub genes might can serve as therapeutic targets for diagnosis of hypertension in the future.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90742886","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

Research on Technical Standards of Prevention and Control of Low Vision among Adolescents 青少年低视力防治技术标准研究

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469680

Wei Pan

{"title":"Research on Technical Standards of Prevention and Control of Low Vision among Adolescents","authors":"Wei Pan","doi":"10.1145/3469678.3469680","DOIUrl":"https://doi.org/10.1145/3469678.3469680","url":null,"abstract":"This paper focuses on adolescents with low vision and indicates that prevention and treatment of adolescents with low vision through standardization methods is urgently needed. By investigating relevant standards and information released through professional organizations such as the International Organization for Standardization (ISO) , the American Optometry Association (AOA), the American Academy of Ophthalmology (AAO), and the International Commission on Illumination (ICI), combined with current status of development of relevant Chinese national standards, sector standards and local standards, the problems of technical standards in development status quo of prevention and control related to adolescents with low vision has been raised in this article. Three aspects can be improved include lacking of environmental standards, the scope of product standards need to be expanded and the Traditional Chinese Medicine standards need to be developed for better prevention and control of low vision among adolescents. It has been pointed out that a standard system with clear goals, complete sets, appropriate levels and clear divisions continuous optimizing and adjusting following social, economic and technical development needs to be established and it can play an important role in promoting the prevention and control of adolescents with low vision.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88634105","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

Identifying Enhancers and Their Strength Based on PCWM Feature by A Two-Layer Predictor 基于PCWM特征的两层预测器识别增强子及其强度

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469707

Huan Yang, Shunfang Wang

{"title":"Identifying Enhancers and Their Strength Based on PCWM Feature by A Two-Layer Predictor","authors":"Huan Yang, Shunfang Wang","doi":"10.1145/3469678.3469707","DOIUrl":"https://doi.org/10.1145/3469678.3469707","url":null,"abstract":"Enhancers are a small region of DNA that can bind with protein. After binding with protein, gene transcription will be strengthened. It is time-consuming and expensive to identify enhancers using traditional biological experimental methods. However, with the development of computer technology, more and more computer technology is applied to gene identification. There are two innovations in this study. First, a new feature information PCWM is proposed, which combines the normalized frequency information of k-tuple nucleotide in DNA sequence as weight and the physicochemical properties of k-tuple nucleotide to obtain DNA sequence features. Second, a two-layer model is proposed to process the acquired sequence feature information to predict the enhancer and its strength. The independent set test results show that this new feature method effectively improves the prediction accuracy of enhancers and their strengths, obtaining accuracy of 77.0% and 69.5%, respectively. Compared with the classical two feature methods, the new feature method shows greater advantages, and has greater improvement than the prediction results of the existing literature. This method is an effective supplement to the existing research methods.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74318446","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

Localizing Epileptic Focus of Patients with Epilepsy Using Post-Ictal Scalp EEG 颅后脑电图在癫痫患者癫痫病灶定位中的应用

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469684

M. Yao, Chunsheng Li

{"title":"Localizing Epileptic Focus of Patients with Epilepsy Using Post-Ictal Scalp EEG","authors":"M. Yao, Chunsheng Li","doi":"10.1145/3469678.3469684","DOIUrl":"https://doi.org/10.1145/3469678.3469684","url":null,"abstract":"Localization of the epileptic focus is crucial for epilepsy surgery. Pre-ictal EEG and interictal epileptic discharges are commonly used to localize the focus. Post-ictal scalp EEG may provide useful information for localizing the epileptic focus. This study proposed a non-invasive procedure to localize the epileptic focus via the EEG source imaging (ESI) and epileptic network analysis. Scalp EEG from two patients with drug-resistant epilepsy were used, and two segments of post-ictal EEG were analyzed. The sLORETA algorithm was applied to obtain signals in source space using the patient specific head model. Then we extracted the representative source signals of each brain area by singular value decomposition (SVD). The epileptic networks of different frequency bands in the source space were constructed by Granger causality analysis. The results showed that the regions identified by in-degree feature of low-frequency post-ictal epileptic network were concordant with surgical resected areas. The preliminary result indicates that post-ictal epileptic network in low frequency may potentially be used to identify the ictal focus for surgical planning.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73868078","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

Abnormal Symbolic Transfer Entropy in Depression

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469718

Yangting Zhang, Yu-xi Luo

引用次数: 0

EEG-Based Depression Recognition Using Intrinsic Time-scale Decomposition and Temporal Convolution Network 基于脑电图的抑郁症识别——基于内在时间尺度分解和时间卷积网络

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469683

Yixin Wang, Fengrui Liu, Lijun Yang

{"title":"EEG-Based Depression Recognition Using Intrinsic Time-scale Decomposition and Temporal Convolution Network","authors":"Yixin Wang, Fengrui Liu, Lijun Yang","doi":"10.1145/3469678.3469683","DOIUrl":"https://doi.org/10.1145/3469678.3469683","url":null,"abstract":"The diagnosis and treatment of depression is very important since it brings a heavy burden to family and society. Because of the high sensitivity, relatively low cost, and convenient recording, electroencephalogram (EEG) has become an important tool for monitoring brain activity and is gradually being used in the auxiliary diagnosis of mental diseases. EEG signals are typically non-linear and non-stationary. Therefore, they are suitable to be dealt with by time-frequency analysis technique. In this paper, we propose a strategy that combines the time-frequency analysis technique and temporal convolution network for depression recognition. Firstly, we use the method of intrinsic time-scale decomposition to decompose each EEG recording to several components. And secondly, some statistical indices are calculated from the instantaneous amplitudes and instantaneous frequencies of these components to form the feature vectors. Thirdly, an improved temporal convolution network (TCN) is used to detect the depression from normal controls. Temporal convolution network is not only suitable for the sequence model, but also retains the parallel computing characteristics of the convolutional neural network. To improve the model performance, we further modify the original softmax loss of TCN as L-softmax. Experiments show the effectiveness of the proposed model. Furthermore, we find that the depressive patients and normal controls shows different patterns through functional connectivity analysis. Our analysis results can be used as an auxiliary tool to help psychiatrists diagnose patients with depression.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78188230","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: 4

Multimodal Optimization Evolutionary Algorithm for RNA Secondary Structure Prediction RNA二级结构预测的多模态优化进化算法

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469714

Yunfei Hu, Kai Zhang

引用次数: 2

Preliminary Study of Local Gene Network in Epilepsy 癫痫局部基因网络的初步研究

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469688

Tianhe Zhang, Le Ao, Bo Sun, Chuyi Zhang, W. Su, Xingfeng Du, Changlu Guo, Yu Yang

引用次数: 0

Melt-based Electrohydrodynamic Bioprinting:: Heating Unit, Ambient Control, and Power Module Control 基于熔体的电流体动力生物打印:加热单元、环境控制和电源模块控制

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469697

Yu-lin Dong, Jie Sun

引用次数: 0

Overview of Machine Learning Methods for Genome-Wide Association Analysis 全基因组关联分析的机器学习方法综述

The Fifth International Conference on Biological Information and Biomedical Engineering Pub Date : 2021-07-20 DOI: 10.1145/3469678.3469682

Minzhu Xie, Fang Liu

引用次数: 0