2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics最新文献

Using Genetic Algorithms to Model Microbiome Coevolutionary Dynamics and Dysbiosis Due to Environmental and Pharmaceutical Stressors 利用遗传算法模拟微生物组共同进化动力学和生态失调由于环境和药物的压力

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448343

Mithra V. Karamchedu

{"title":"Using Genetic Algorithms to Model Microbiome Coevolutionary Dynamics and Dysbiosis Due to Environmental and Pharmaceutical Stressors","authors":"Mithra V. Karamchedu","doi":"10.1145/3448340.3448343","DOIUrl":"https://doi.org/10.1145/3448340.3448343","url":null,"abstract":"Several studies have established the critical role of microbiome in shaping human health. Steady state balance, a microbial homeostasis, of disparate microbial colonies is the outcome of coevolution and affects the continued health, chronic disease or susceptibility to ill-health. Environmental stressors, including infection and pharmaceuticals, can trigger imbalance and maladaptation of these microbial colonies. Microbial populations of related species are often associated with a specific biological outcome due to a shared biological function (clustered signal). Similarly, diverse interdependent species are also associated with a specific biological outcome (dense signal). When either deliberate or inadvertent influences disrupt the stable relative population of microbes, understanding the dynamics of coevolution in the altered state is important if we are to ultimately understand the longer-term effects of such a disruption. This study attempts to create a generalized approach to model the coevolutionary dynamics of the microbiome due externally triggered disruptions. Preliminary results suggest that the model is successful in simulating stable relative compositions and evaluating pair-wise competition/cooperation scores for microbiome species. The results support the prospect of simulating and predicting the prevalence of Inflammatory Bowel Disease (IBD) as a result of co-evolutionary dynamics. The results further support the possibility of using such a computational approach to model antibiotic induced disruptions to the microbiome.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121200508","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

Constructing Crystalline Molecular Dipolar Rotor Arrays with Ultra-Large Dipole Moments 构建具有超大偶极矩的晶体分子偶极转子阵列

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448352

Tong Gao

引用次数: 0

Evaluation of Upper Limb Muscle Function Based on Muscle Activation Sequence 基于肌肉激活序列的上肢肌肉功能评价

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448348

Zihan Wang, Yingfei Sun, Jiankang Wu, Kunkun Zhao

引用次数: 0

Motor Imagery EEG Classification Method Based on Adaptive Decision Surface of LDA Classifier 基于LDA分类器自适应决策面的运动意象脑电分类方法

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448346

Banghua Yang, Du Li, Baiheng Ma, Xuelin Gu, Dewen Kong

{"title":"Motor Imagery EEG Classification Method Based on Adaptive Decision Surface of LDA Classifier","authors":"Banghua Yang, Du Li, Baiheng Ma, Xuelin Gu, Dewen Kong","doi":"10.1145/3448340.3448346","DOIUrl":"https://doi.org/10.1145/3448340.3448346","url":null,"abstract":"In order to solve the problem of reduced classification accuracy caused by the different spatial features of EEG motor imagery signals between the same subjects on different days, this paper proposes an EEG feature extraction and recognition method based on common spatial pattern (CSP) and decision surface adaptive linear discriminant analysis (DSALDA). The decision surface threshold of the linear discriminant analysis (LDA) classifier was updated by saving the CSP spatial feature of the different day's test data, and use parameters to control the proportion of CSP spatial features between the training data set and the different day’ test data set. The experiment collected EEG data of 24 subjects, each subject collected data on different days. The results show that the average accuracy of this method is improved by 6.35% compared with the CSP-LDA method, which effectively improves the accuracy and stability of the different day's motor imagery EEG classification. It has created conditions for the wide application of motor imagery brain-computer interface (BCI) system in the field of rehabilitation.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495675","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: 1

High CD34 Expression Level Associated with Prolonged Liver and Stomach Cancer Survival CD34高表达水平与肝癌和胃癌生存期延长相关

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448350

Xin Sun

{"title":"High CD34 Expression Level Associated with Prolonged Liver and Stomach Cancer Survival","authors":"Xin Sun","doi":"10.1145/3448340.3448350","DOIUrl":"https://doi.org/10.1145/3448340.3448350","url":null,"abstract":"CD34 is a transmembrane phosphoglycoprotein that shows expression on early hematopoietic and vascular-associated tissues. Although tumor tissues usually possess upregulated CD34, little is known about this protein's exact function during the process of tumor development. Here, we carried out this study to find possible associations between tumor survival and the overexpressed CD34. We focused our data on patients with adenomas (and adenocarcinomas) occurring in liver or stomach, as these cancer types are common worldwide and contain a large amount of available patient statistics recorded in the TCGA database and we found that CD34 was among the group of proteins under significant upregulation in liver and stomach cancer. However, after constructing the survival curves comparing patient survival of high-level CD34 expression with low-level CD34 expression through a period of time, we observed that high CD34 expression level actually associates with a better tumor survival. This counter-intuitive relation between CD34 and patient survival rate implies a complicated cause-effect relationship behind, which suggests that the idea of designating CD34 as the target to develop therapies for treating cancer still needs more evaluations.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396359","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

The Progress of IPS Therapy on the Heart and Neural Disease IPS治疗心脏和神经系统疾病的研究进展

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448347

Chang-Quan Sun

引用次数: 0

Machine Learning Facilitates Imputation of Gene Expression Levels across Multiple Environments 机器学习促进了跨多种环境的基因表达水平的Imputation

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448342

Ziang Xu, H. Qi

{"title":"Machine Learning Facilitates Imputation of Gene Expression Levels across Multiple Environments","authors":"Ziang Xu, H. Qi","doi":"10.1145/3448340.3448342","DOIUrl":"https://doi.org/10.1145/3448340.3448342","url":null,"abstract":"Gene expression level reflects the active biological processes in a live cell. It is of great importance to quantify gene expression levels across multiple environments. However, for technical reasons, the expression level in some environments/strains of species may not be measured correctly because of sequence diversity or technical reasons in mRNA-seq, qPCR, or microarray. Therefore, it would be highly beneficial if we could infer the missing expression level from existing data, and this process of filling in such missing values is called imputation. Imputation is a very active field in machine learning, and many tech companies use imputation to infer customer preferences for products/movies, etc. Here we apply multiple state-of-the-art imputation methods and compare their performance in predicting gene expression levels across multiple environments. Using a multi-environment expression dataset of Saccharomyces cerevisiae across 13 environments, we randomly removed 5%, 20%, 50%, and 75% of the expression level from the dataset and applied various imputation methods to predict the missing values and use root mean squared error for comparison of model performances. We found that SVD works the best among the five methods, followed by KNN with five nearest neighbors and KNN with two nearest neighbors. In contrast, univariate mean and univariate median works the worse and perform similarly. Although the latter two univariate methods were very commonly used in practice, our result highlights the benefit of using machine learning methods for imputation for better predictions of expression levels across environments.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128726891","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

Rapid Identification of Potential Inhibitors of Bruton's Tyrosine Kinase (BTK) by Structure-based Virtual Screening 基于结构的虚拟筛选快速鉴定布鲁顿酪氨酸激酶(BTK)潜在抑制剂

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448345

Yuhao Wang

引用次数: 0

Automatic Assistance Method for Disease Diagnosis Based on a Deep Learning Fusion Model and Chinese Electronic Medical Record 基于深度学习融合模型和中文电子病历的疾病诊断自动辅助方法

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448341

Yiping Wang, Guixia Kang, Lijun Liu, Qingsong Huang

{"title":"Automatic Assistance Method for Disease Diagnosis Based on a Deep Learning Fusion Model and Chinese Electronic Medical Record","authors":"Yiping Wang, Guixia Kang, Lijun Liu, Qingsong Huang","doi":"10.1145/3448340.3448341","DOIUrl":"https://doi.org/10.1145/3448340.3448341","url":null,"abstract":"Extracting disease characteristics from large-scale Electronic Medical Records and achieving disease-assisted diagnoses have significant research value. Due to the complex multi-feature items and unbalanced data distribution of Electronic Medical Records, feature representation and disease diagnosis are difficult. Our study proposes a deep feature fusion (DFF) model based on the feature partition and deep feature extraction. First, the feature partition is performed, and different feature representation algorithms are adopted for different types of data. The discrete feature items are directly mapped into real-valued vectors, and the continuous feature items are represented by GCNN-based VAE. Then, the two parts are fused. Finally, the assisted diagnosis results are output through a supervised learning classification method based on the XGBoost framework. The dataset of our study is from the 18,590 real and effective clinical Electronic Medical Record of Huangshi Central Hospital. The experimental results show that the method can perform clinical Assisted diagnosis accurately and efficiently, which are superior to some other state-of-the-art approaches, can better meet the needs of practical clinical diagnosis applications.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126506242","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

Investigation of Potential Inhibitors of Brown Adipose Tissue Induced Thermogenesis 棕色脂肪组织诱导产热的潜在抑制剂的研究

2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics Pub Date : 2021-01-09 DOI: 10.1145/3448340.3448349

Chang-xun Chen

引用次数: 0