2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)最新文献_第2页

Drug Resistance Testing Using Electrical Impedance Counting Method 电阻抗计数法耐药试验

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061299

Jindai Huang, Dianchen Zhang

{"title":"Drug Resistance Testing Using Electrical Impedance Counting Method","authors":"Jindai Huang, Dianchen Zhang","doi":"10.1109/ISBP57705.2023.10061299","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061299","url":null,"abstract":"Traditional methods for testing drug sensitivity values include broth dilution methods including micro broth dilution and macro broth dilution methods, agar dilution methods, E-test methods and paper diffusion methods[1]. These traditional methods have a common shortcoming, that is, the test often takes a long time to obtain drug sensitivity results, which can lead to missed diagnosis, misdiagnosis and delayed treatment.This paper describes how the electrical impedance counting method was used for the study of drug sensitivity testing and what advantages it has over traditional drug sensitivity testing. The impedance counting method still uses the broth dilution method and the existing parameter MIC (minimum inhibitory concentration)[1], which is the most relied upon parameter to guide the use of antibiotics, as an indicator, with the Coulter principle[2] technique as the underlying principle, and the IT system to collect, process and analyze the data. The MIC values were obtained by calculating the number of bacteria at different doses of antibiotics, analyzing the graphical changes such as growth indices, using software algorithms and referring to the CLSI M100[3] standard to discriminate the drug susceptibility of bacteria. The results show that the electrical impedance counting method will provide us with a more rapid and effective method for drug sensitivity analysis, and can be used in medical institutions and related medical industries for drug sensitivity analysis, which is of great significance for rapid and accurate clinical drug use and new antibiotic research.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517914","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

Box-Behnken Designs for the Optimization of the Ethanol Extraction Process for Chuilian Jianpi Granules 疏连健脾颗粒乙醇提取工艺的Box-Behnken设计优化

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061319

Yingying Wang, Guang-Jiao Zhou, Xiao-Wei Li

引用次数: 0

Semi-supervised Medical Image Segmentation with Low-Confidence Consistency and Class Separation 基于低置信度一致性和类分离的半监督医学图像分割

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061306

Zhimin Gao, Tianyou Yu

{"title":"Semi-supervised Medical Image Segmentation with Low-Confidence Consistency and Class Separation","authors":"Zhimin Gao, Tianyou Yu","doi":"10.1109/ISBP57705.2023.10061306","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061306","url":null,"abstract":"Deep learning has achieved a great success in various fields, such as image classification, semantic segmentation and so on. But its excellent performance tends to rely on a large amount of data annotations that are hard to collect, especially in dense prediction tasks, like medical image segmentation. Semi-supervised learning (SSL), as a popular solution, relieves the burden of labeling. However, most of current semi-supervised medical image segmentation methods treat each pixel equally and underestimate the importance of indistinguishable and low-proportion pixels which are drowned in easily distinguishable but high-proportion pixels. We believe that these regions with less attention tend to contain crucial and indispensable information to obtain better segmentation performance. Therefore, we propose a simple but effective method for semi-supervised medical image segmentation task via enforcing low-confidence consistency and applying low-confidence class separation. Concretely, we separate low- and high-confidence pixels via the maximum probability values of model’s predictions and only low-confidence pixels are kept. For these remaining pixels, in the mean teacher framework, consistency is enforced for invariant predictions between student and teacher in the output level, and class separation is applied for promoting representations close to corresponding class prototypes in the feature level. We evaluated the proposed approach on two public datasets of cardiac, achieving a higher performance than the state-of-the-art semi-supervised methods on both datasets.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129948867","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

Intelligent Compound Selection of Anti-cancer Drugs Based on Multi-Objective Optimization 基于多目标优化的抗癌药物智能复方选择

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061321

Xiaoyan Liu, Zhiwei Xu, Guangwen Liu, Limin Liu

{"title":"Intelligent Compound Selection of Anti-cancer Drugs Based on Multi-Objective Optimization","authors":"Xiaoyan Liu, Zhiwei Xu, Guangwen Liu, Limin Liu","doi":"10.1109/ISBP57705.2023.10061321","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061321","url":null,"abstract":"In the compound selection process of anti-cancer drugs, safety properties such as drug activity and pharmacokinetics need to be considered simultaneously. To construct a more complete and precise drug screening mechanism, this paper proposed an intelligent compound selection method for anti-cancer drugs based on multi-objective optimization. The proposed model is executed in the MapReduce environment. Quantitatively analyze the biological activity of the compound, and qualitatively analyze the properties of pharmacokinetics and safety properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to build a multi-objective optimization model. Guided by Pareto optimization theory, the set of non-inferior solution values was determined, and the compound combination that satisfies the optimization goal was found by genetic optimization. On this basis, a Monte Carlo hypothesis test was used to determine the equipped range of the compounds. Finally, an example of the compound selection of anti-breast cancer drugs is given, and the experimental evaluation proves that the algorithm can screen compounds limitedly, which provides a basis for anti-cancer drug synthesis.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126733326","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 virtual reality technology in post-traumatic stress disorder 虚拟现实技术在创伤后应激障碍中的应用

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061313

Jinxiu Zhang, Xunbing Shen

引用次数: 1

ISBP 2023 Cover Page ISBP 2023封面页

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/isbp57705.2023.10061300

引用次数: 0

High-efficiency drug design research based on virtual high-throughput screening 基于虚拟高通量筛选的高效药物设计研究

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061293

Haonan Zhou

引用次数: 0

ECG arrhythmias Classification with a Graph Bispectrum method 基于图双谱法的心电心律失常分类

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061314

Yang Shiyilin, Shao Jie, Yang Xin, Chen Xin, Wang Xingxing

引用次数: 0

Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports 基于集成条件项支持的深度优先不确定频繁项集挖掘

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061307

Wanyong Tian, Fuqiang Li, Yibo Liu, Zichen Wang, Zhang Tao

{"title":"Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports","authors":"Wanyong Tian, Fuqiang Li, Yibo Liu, Zichen Wang, Zhang Tao","doi":"10.1109/ISBP57705.2023.10061307","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061307","url":null,"abstract":"Uncertain frequent pattern mining is usually challenged by the single probabilistic frequent threshold or the single expected support as the measurements of frequent itemsets. A promising solution based on multiple expected minimum support has been introduced in more recent studies to distinguish the mining values of each item, but the intrinsic combinatorial explosion still limited this strategy to be further improved for more generic scenarios. In this paper, a novel mining scheme for uncertain frequent itemsets is proposed. By ensembling multiple conditional item-wise supports, the problems of information redundancy as well as loss caused by a single probabilistic frequent threshold can be effectively improved. Furthermore, by using a variety of pruning strategies based on the property of sorted downward closure and the concept of least minimum probabilistic frequent threshold, an UFP-ECIS (Uncertain Frequent Pattern Mining with Ensembled Conditional Item-wise Supports) algorithm is also introduced. Substantial experiments have been proved to demonstrate that the proposed mining scheme and algorithm has enhanced the information precision of the uncertain frequent itemsets mining.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126460151","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

EEG Motion Classification Combining Graph Convolutional Network and Self-attentiion 结合图卷积网络和自注意的脑电运动分类

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI: 10.1109/ISBP57705.2023.10061298

Lingyun Chen, Yi Niu

{"title":"EEG Motion Classification Combining Graph Convolutional Network and Self-attentiion","authors":"Lingyun Chen, Yi Niu","doi":"10.1109/ISBP57705.2023.10061298","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061298","url":null,"abstract":"The study of EEG motor imagery adds a new therapeutic approach for patients with motor disorders, and the key to the problem study is how to improve the classification recognition of EEG motor imagery. The complex characteristics of EEG signals and the existence of multi-channel spatio-temporal properties increase the difficulty of their feature extraction and classification. There are spatial correlations between different channels and temporal correlations between different time series signals, so the selection process of signal features is complicated, resulting in low recognition rate. In this paper, we propose a spatial graph convolutional neural network based on a self-attentive mechanism. For the spatial characteristics of signals with different channels, we extract spatial features by constructing a graph structure and then by information aggregation; for its temporal characteristics, we use time slicing to calculate the importance weights of different time periods in the input signal by using the self-attentive mechanism, and then update the time segments by weighting and summing, so as to minimize the influence of other interfering signals, complete feature extraction and improve the The classification recognition rate is improved. From the experimental results, the recognition rate of this model reaches over 88% in the existing open EEG motion imagery dataset, which has good practicality and applicability.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132947242","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