2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第4页

Aritificial Inteligence Challenges in COPD management: a review 人工智能在COPD管理中的挑战:综述

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635374

L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević

{"title":"Aritificial Inteligence Challenges in COPD management: a review","authors":"L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević","doi":"10.1109/BIBE52308.2021.9635374","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635374","url":null,"abstract":"Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412919","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

The Review of Materials for Energy Harvesting 能量收集材料研究进展

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635169

Miloš Anić, Momcilo Prodanovic, S. Milenkovic, Nenad D Filipović, N. Grujovic, F. Živić

引用次数: 1

Harmonization of Multi-site Dynamic Functional Connectivity Network Data 多站点动态功能连接网络数据的协调

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635538

Biozid Bostami, V. Calhoun, H. V. D. Horn, V. Vergara

引用次数: 5

Multiple-Activation Parallel Convolution Network in Combination with t-SNE for the Classification of Mild Cognitive Impairment 多重激活并行卷积网络联合t-SNE对轻度认知障碍的分类

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635485

Harsh Bhasin, R. Agrawal

{"title":"Multiple-Activation Parallel Convolution Network in Combination with t-SNE for the Classification of Mild Cognitive Impairment","authors":"Harsh Bhasin, R. Agrawal","doi":"10.1109/BIBE52308.2021.9635485","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635485","url":null,"abstract":"The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085782","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

Preparation of Simplified Molecular Input Line Entry System Notation Datasets for use in Convolutional Neural Networks 用于卷积神经网络的简化分子输入行输入系统符号数据集的制备

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635320

Sandi Baressi Segota, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Z. Car

引用次数: 3

Numerical Simulation of Sedimentation Process using Mason-Weaver Equation 基于Mason-Weaver方程的沉降过程数值模拟

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635216

Milica G. Nikolić, T. Šušteršič, Nenad Filipović

引用次数: 0

Computational Modeling of Sarcomere Protein Mutations and Drug Effects on Cardiac Muscle Behavior 肌节蛋白突变的计算模型和药物对心肌行为的影响

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635428

Momcilo Prodanovic, B. Stojanovic, Danica Prodanovic, N. Filipovic, S. Mijailovich

引用次数: 1

Convolutional Neural Networks for Cellular Drug Response Prediction Using Immunofluorescence Images of Intracellular Actin Filament Networks 基于细胞内肌动蛋白丝网络免疫荧光图像的卷积神经网络药物反应预测

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635241

R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu

{"title":"Convolutional Neural Networks for Cellular Drug Response Prediction Using Immunofluorescence Images of Intracellular Actin Filament Networks","authors":"R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu","doi":"10.1109/BIBE52308.2021.9635241","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635241","url":null,"abstract":"Actin cytoskeleton has been identified as a potential therapeutic target for cancer. Therefore, to identify cell responses to such chemical agents has been an essential part in the past studies, which is often measured visually. This kind of visual recognition task currently is performed by human experts, which poses a great challenge since the features can hardly be detected using only human eyes. This article presents the application of convolutional neural networks (CNNs) in classifying human breast epithelial cells based on different dosages of drug exposure. MCF-10A cell line was chosen for the experiments and was treated with 90 nM and 400 nM cytochalasin D. The CNNs were evaluated on a large immunofluorescence images of intracellular actin filament networks captured after the exposure of different drug concentrations. During the image pre-processing, we implemented image enhancement and data augmentation approaches. Two well-known CNNs, VGG-16 and ResNet-50, were trained with or without transfer learning. The study revealed that the CNN performed better in the classification task compared to human experts. In conclusion, ResN et-50 with transfer learning achieved the best performance.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596548","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

Estimation of antiradical properties of series of 4, 7 - dihydroxycoumarin derivatives towards DPPH radical-experimental and DFT study 一系列4,7 -二羟基香豆素衍生物对DPPH自由基的抗自由基性能评价-实验和DFT研究

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635257

Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković

{"title":"Estimation of antiradical properties of series of 4, 7 - dihydroxycoumarin derivatives towards DPPH radical-experimental and DFT study","authors":"Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković","doi":"10.1109/BIBE52308.2021.9635257","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635257","url":null,"abstract":"Different phenolic coumarin derivatives represent a widespread class of compounds that have shown remarkable activity in removing reactive oxygen species. For this reason, within this study, the antiradical activity of previously synthesized phenolic derivatives of 4,7 -dihydroxycoumarin: (E)-3-(1-((2-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-20H), $(E)$ -3-(1((3-hydroxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (A-30H), $(E)$. -3-(1((4-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-40H) acetate against the 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical was investigated. All research is supported by Density Functional Theory $(mathbf{DFT}/mathbf{M06}-mathbf{2X/6-311++}mathbf{G}(mathbf{d, p})$ level of theory and CPCM solvation model-methanol) in combination with global chemical reactivity parameters. The results of experimental scavenging activity towards DPPH· indicate that A-20H shows the best activity. The most probable scavenging route was determined based on the thermodynamic parameters. A good correlation between experiment and theory showed that Hydrogen Atom Transfer (HAT, $Deltatext{rGHAT}$) was the dominant pathway of the reduction of DPPH·. In general, the results of global chemical reactivity parameters show that the A-40H compound shows the best electron-donating properties, which is correlated with thermodynamic parameters obtained for the Single Electron Transfer (SET, $Delta{text{rGSET}}$) mechanism.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123699361","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

Clustering based Segmentation of MR Images for the Delineation and Monitoring of Multiple Sclerosis Progression 基于聚类分割的磁共振图像用于多发性硬化症进展的描绘和监测

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635369

Styliani P. Zelilidou, E. Tripoliti, Kostas I. Vlachos, S. Konitsiotis, D. Fotiadis

引用次数: 0