2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)最新文献_第3页

Fatigue Detection in Running with Inertial Measurement Unit and Machine Learning 基于惯性测量单元和机器学习的运行疲劳检测

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802471

Guodong Wang, Xiaokun Mao, Qiuxia Zhang, Aming Lu

{"title":"Fatigue Detection in Running with Inertial Measurement Unit and Machine Learning","authors":"Guodong Wang, Xiaokun Mao, Qiuxia Zhang, Aming Lu","doi":"10.1109/icbcb55259.2022.9802471","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802471","url":null,"abstract":"To date athlete/patient fatigue has been assessed using expensive laboratory equipment. Inertial measurement unit (IMU) offer an opportunity to provide low-cost and non-intrusive fatigue assessment. The aim of this study was to determine if in combination or in isolation, IMUs positioned on the low extremities are capable of distinguishing between fatigued and un-fatigued running states and to predict the degree of fatigue. A running fatigue dataset based on multiple IMUs was constructed by recording inertial data during running to a state of fatigue. In addition to the inertial data from the IMUs, the perceived level of exertion was monitored for each participant as an indication of their physical fatigue level. Random forest (RF) and support vector machine (SVM) model validation was performed on the dataset to classify the running fatigue and fatigue levels. Classification effect of RF was better than SVM; the classification accuracy improved with the increase of sensors; the accuracy of tibial IMU data on RF accomplished 87.21%; the classification accuracy of combination of tibia and thigh IMUs was the highest at 91.10%. This study highlights the potential of inertial sensor to objectively estimate the level of fatigue during running by detecting minor deviations in lower extremity biomechanics.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124062567","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: 2

Bioinformatics Analysis of the Structure and Function of EdeB from Brevibacillus brevis X23 短芽孢杆菌X23 EdeB结构与功能的生物信息学分析

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802134

J. Du, Cuiyang Zhang, Qingshan Long, Wu Chen, Zhaohui Guo, Qingshu Liu

{"title":"Bioinformatics Analysis of the Structure and Function of EdeB from Brevibacillus brevis X23","authors":"J. Du, Cuiyang Zhang, Qingshan Long, Wu Chen, Zhaohui Guo, Qingshu Liu","doi":"10.1109/icbcb55259.2022.9802134","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802134","url":null,"abstract":"Brevibacillus Brevis X23 is widely used in the biological control of plant diseases. It can produce antibacterially active substances—edeines. EdeB protein is a potential regulator of edeines biosynthesis. Analyzing its bioinformatics information may lay a foundation for the further study of the function of EdeB protein in the process of antibiotic biosynthesis. Based on the amino acid sequence of EdeB protein from NCBI database, the bioinformatics analyses were performed to analyze its protein physical and chemical properties, transmembrane region, signal peptide, tertiary structure, phosphorylation sites and glycosylation sites. The results showed that EdeB protein was composed of 256 amino acids. It had a relative molecular weight of 30.42 kDa and a theoretical isoelectric point (pI) of 6.23. It was a hydrophilic protein without signal peptide or transmembrane. Its secondary structure mainly consisted of α-helices and random coil. The tertiary structural model for EdeB protein was successfully built. The EdeB protein belonged to the ParB family, suggesting its biological function of regulation by DNA-binding. It was predicted to have 16 phosphorylation sites, 1 N-glycosylation site and 20 O-glycosylation sites. This study can provide a theoretical basis for the function and mechanism of action of EdeB protein.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124178943","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: 2

Multiplicative Seasonal ARIMA Modeling and Forecasting of COVID_19 Daily Deaths in Hungary 匈牙利covid - 19每日死亡人数的乘法季节性ARIMA模型和预测

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802498

Solomon Buke Chudo

{"title":"Multiplicative Seasonal ARIMA Modeling and Forecasting of COVID_19 Daily Deaths in Hungary","authors":"Solomon Buke Chudo","doi":"10.1109/icbcb55259.2022.9802498","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802498","url":null,"abstract":"The coronavirus disease (COVID-19) is a terrifying pandemic that is rapidly spreading over the world. Up to this point, Hungary has had a significant COVID-19 death rate. The main purpose of this article is to model and forecast basic seasonal time series for COVID-19 death rates. The COVID 19 data, which was collected between 2020-10-04 and 2021-05-12 by the Hungarian government and the World Health Organization (WHO), has been used. The data was analyzed and models were fitted using R software version 4.1.2. The statistical time series model is fitted with the Multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Forecasts are made using the fitted model. The data output is used to find seasonality, trend patterns, and unstable variance patterns in the time series plot. The trend is made stationary using the starting difference of the converted data approach, and the variance is made constant using the logarithmic transformation of the original data set. Based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plot data, the ARIMA (1, 1, 2) (1, 0, 1) (7) model is proposed. The standardized residuals, ACF of residuals, normal Q-Q plot, and p-value for Ljung-Box statistics of the fitted model were found to be within confidence limits and to have no distinct behavioral pattern. The ARIMA (1, 1, 2) (1, 0, 1) (7) model has the smallest estimated value, with a sigma square estimated value of 0.02764, log-likelihood = 80.41, and an Akaike Information Criterion (AIC) value of 148.82. As a consequence, the fitted model ARIMA (1,1,2) (1,0,1) (7) is identified as the best model for forecasting the COVID-19 daily death rate in the country.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"982 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127792676","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

Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling 通过随机抽样和机器学习建模的共识评分发现胃腺癌生物标志物

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802469

Ji Chen, Yang Hao, Tianjun Wang, Daiyun Huang, Xin Liu

引用次数: 0

Predicting Structural Similarity between Molecules Using Graph Neural Networks 使用图神经网络预测分子之间的结构相似性

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802484

Sichen Deng, Yŏng-ik Yu

引用次数: 0

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification SubOmiEmbed:用于癌症类型分类的多组学数据自监督表示学习

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-02-03 DOI: 10.1109/icbcb55259.2022.9802478

S. Hashim, Muhammad Ali, K. Nandakumar, Mohammad Yaqub

{"title":"SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification","authors":"S. Hashim, Muhammad Ali, K. Nandakumar, Mohammad Yaqub","doi":"10.1109/icbcb55259.2022.9802478","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802478","url":null,"abstract":"For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) [1] have been extensively used in embedding image and text data into lower dimensional latent spaces. In our work, we extend the idea of using a VAE model for low dimensional latent space extraction with the self-supervised learning technique of feature subsetting. With VAEs, the key idea is to make the model learn meaningful representations from different types of omics data, which could then be used for downstream tasks such as cancer type classification. The main goals are to overcome the curse of dimensionality and integrate methylation and expression data to combine information about different aspects of same tissue samples, and hopefully extract biologically relevant features. Our extension involves training encoder and decoder to reconstruct the data from just a subset of it. By doing this, we force the model to encode most important information in the latent representation. We also added an identity to the subsets so that the model knows which subset is being fed into it during training and testing. We experimented with our approach and found that SubOmiEmbed produces comparable results to the baseline OmiEmbed [2] with a much smaller network and by using just a subset of the data. This work can be improved to integrate mutation-based genomic data as well.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122604329","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: 2