2022 2nd International Conference on Bioinformatics and Intelligent Computing最新文献_第7页

Modeling and Simulation of Input Signal of STDP Synaptic System STDP突触系统输入信号的建模与仿真

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524580

Yumei Gong

引用次数: 0

Study on the Causal Relationship of Cardiovascular Disease Influencing Factors Based on Bayesian Causal Network 基于贝叶斯因果网络的心血管疾病影响因素因果关系研究

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524529

Jin Wang, Yaping Wan

{"title":"Study on the Causal Relationship of Cardiovascular Disease Influencing Factors Based on Bayesian Causal Network","authors":"Jin Wang, Yaping Wan","doi":"10.1145/3523286.3524529","DOIUrl":"https://doi.org/10.1145/3523286.3524529","url":null,"abstract":"The WHO MONICA dataset was used as an example, and a logistic regression model was used to statistically analyze the data, and then a Bayesian causal network model was constructed using the MMHC hybrid algorithm to analyze the causal relationships among cardiovascular disease risk factors, and Bayesian estimation was used to learn the conditional probabilities of each node of the network so as to predict the survival of patients, and to compare the Bayesian causal network model with respect to logistic regression model in the field of chronic diseases. Bayesian causal network model results showed that hospitalization status, age at diagnosis, and angina status were direct causes of cardiovascular mortality, while previous myocardial infarction, sex, and smoking had indirect effects on cardiovascular mortality through other variables. Compared to logistic regression models, Bayesian causal networks based on the MMHC algorithm are more applicable in clinical research because they can intuitively and effectively identify and define the complex causal relationships between survival outcomes and cardiovascular disease and cardiovascular disease with each other. By analyzing this relationship, we are able to implement timely and targeted preventive and therapeutic measures and avoid possible mortality outcomes in high-risk populations.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123503130","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

Application of Convolutional Neural Network in COVID-19 Diagnosis 卷积神经网络在新型冠状病毒诊断中的应用

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3523287

Ke Liu, Ran Zhang, Yixuan Wang, Liuqing Shen, Peipei Han, Zhe Chen, YiJun Qi, Shegan Gao

引用次数: 0

Autonomous DNA Neuron Learning Algorithm Based on DNA Strand Displacement 基于DNA链位移的自主DNA神经元学习算法

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524540

Xuedong Zheng, Yang Ru

引用次数: 0

Ecological efficiency in ethnic regions based on super efficiency SBM-Malmquist model 基于超效率SBM-Malmquist模型的民族地区生态效率研究

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524601

Lin Hou

引用次数: 0

Grading Prediction of Kidney Renal Clear Cell Carcinoma by Deep Learning 基于深度学习的肾透明细胞癌分级预测

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524510

Kun Zhou, Liang Wei

{"title":"Grading Prediction of Kidney Renal Clear Cell Carcinoma by Deep Learning","authors":"Kun Zhou, Liang Wei","doi":"10.1145/3523286.3524510","DOIUrl":"https://doi.org/10.1145/3523286.3524510","url":null,"abstract":"The grade of cancer is a way to classify cancer based on certain characteristics of cancer tissue. It is an important issue for the precise diagnosis, treatment, and mechanistic research of cancer. With the rapid development of genome sequencing technology, it has become possible to obtain large amounts of gene expression data, and large-scale genomic data to predict the grade of cancer is a challenging problem. In this study, we used gene expression data to propose a pathway-related deep neural network (K-Net) for predicting the grade of Kidney renal clear cell carcinoma (KIRC) tissues. K-Net provides the capability of model interpretability that most conventional fully-connected neural networks lack, describing which pathways play an important role in the process of predicting grade. The predictive performance of K-Net was evaluated with multiple cross-validation experiments. The K-Net prediction accuracy of 74%. More meaningfully, in contrast to using genes as features, this new classification model using enriched pathways as features can well explain which pathways play an important role in KIRC tissues from highly differentiated to poorly differentiated. Cancer development is a process of degradation of certain functions and enhancement of certain functions of tumor tissue, and understanding which pathways play an important role in cancer development can help explore research directions in cancer treatment.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121271103","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 central arterial pressure estimation algorithm based on clustering 基于聚类的中心动脉压估计算法研究

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524554

Jiahao Zhang, Hui Ge

{"title":"Research on central arterial pressure estimation algorithm based on clustering","authors":"Jiahao Zhang, Hui Ge","doi":"10.1145/3523286.3524554","DOIUrl":"https://doi.org/10.1145/3523286.3524554","url":null,"abstract":"Central arterial pressure (CAP) plays an important role in the detection and diagnosis of cardiovascular diseases. Although the traditional universal transfer function method has good accuracy in measuring central arterial pressure, it does not take into account the characteristic information contained in pulse wave shape, and the accuracy needs to be further improved.Therefore, a clustering based estimation algorithm for central arterial pressure is proposed. First of all, 5 layer Symlets wavelet and hard-soft compromised threshold was used to remove high-frequency noise of the data set. Secondly, in order to extract the characteristics of pulse waves themselves, k-means++ clustering was carried out for pulse waves in the training set. In order to reduce the over-fitting phenomenon, the initial number of cluster centers is selected as 1000. Finally, in each cluster, discrete Fourier transform is performed on pulse wave and central artery wave to obtain amplitude and phase data and train the transfer function. The test set was used for verification. Firstly, the corresponding clustering category of pulse wave was calculated, and then CAP was calculated by transfer function. The results showed that the absolute errors of systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) were 2.50±2.22mmHg, 4.47±2.72mmHg and 4.60±3.73mmHg respectively. Compared with other algorithms, our method has better accuracy.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126051608","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

Noise Pollution Loss Value Evaluation of Railway Transportation Based on Hedonic Price Method- The Case of Taiyuan City 基于享乐价格法的铁路运输噪声污染损失价值评价——以太原市为例

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524564

J. Gong, Ya-nan Gao, Junliang Hao

{"title":"Noise Pollution Loss Value Evaluation of Railway Transportation Based on Hedonic Price Method- The Case of Taiyuan City","authors":"J. Gong, Ya-nan Gao, Junliang Hao","doi":"10.1145/3523286.3524564","DOIUrl":"https://doi.org/10.1145/3523286.3524564","url":null,"abstract":"Noise pollution damages people's health and brings about value loss at the same time. This paper takes Taiyuan City as the research area, selects railway transportation noise as the research object, estimates the marginal implied price of noise, and then estimates the loss value of railway transportation noise pollution in Taiyuan City. The results show that: According to the monitoring results of railway transport noise, the maximum equivalent sound level of the selected residential sample points is 74dB, and the minimum equivalent sound level is 43dB. The noise of most residential buildings near the railway exceeds the Class 1 standard 55dB. In addition, there is a significant negative correlation between the railway transport noise and the unit price of residential buildings around the railway. The regression coefficient of the noise variable is -0.246%. The marginal loss of railway transport noise pollution is calculated according to the valuation formula. It is found that for every 1dB increase in noise value, the pollution loss value caused by noise pollution is 50.8 yuan/square meter, and the impact range of value loss caused by railway transportation noise pollution is about 500m.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125595600","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 Interplay between Grid and Place Cell Networks and Contextual Inputs 网格和位置细胞网络与上下文输入之间的相互作用

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524549

Jiarui Wang

引用次数: 0

Respiration and heartbeat signal separation algorithm using UWB radar platform 基于UWB雷达平台的呼吸与心跳信号分离算法

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524556

Jiawei Cai, Q. Fu, Xue-Feng Yuan, Xiangwei Zhu, Huifu Lin, Yinshen Huang

{"title":"Respiration and heartbeat signal separation algorithm using UWB radar platform","authors":"Jiawei Cai, Q. Fu, Xue-Feng Yuan, Xiangwei Zhu, Huifu Lin, Yinshen Huang","doi":"10.1145/3523286.3524556","DOIUrl":"https://doi.org/10.1145/3523286.3524556","url":null,"abstract":"The human chest wall fretting signal detected by ultra-wideband radar combines chest wall periodic motion caused by breathing and heartbeat. And the frequency and the amplitude of the respiration signal change at any time. We use the empirical model that chest wall motion caused by respiration movement changes sinusoidally to improve the separation effect of respiration and heartbeat waveforms. Based on the hypothesis, we propose an algorithm named adaptive sine wave fitting combined with the baseline drift elimination algorithm to eliminate the respiratory and its high-order harmonic in the time domain. Through our algorithm, the heartbeat spectrum is separable. And the average error of heart rate reduces to 1.586%, which is 24.770% of the high pass filter (HF) method and 13.188% of the CEEMD method. There is also some improvement in the matching effect between the heartbeat and the ECG waveform. Furthermore, we verify the feasibility and accuracy of the algorithm through different breathing patterns. And we find that the SWF algorithm performs more stable in the chest and abdominal breathing mode than the HF method and the CEEMD method.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115225537","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