2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第3页

Determination of VEGF and CXCR4 in Tumor and Peritumoral Tissue of Patients with Breast Cancer as a Predictive Factor 乳腺癌患者肿瘤及瘤周组织中VEGF和CXCR4的检测作为预测因素

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635306

D. Cvetković, A. Cvetkovic, Danijela D. Nikodijević, Jovana V. Jovankić, Milena G. Milutinović, V. Stojić, N. Zdravković, Slobodanka Mltrović

引用次数: 0

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

A Gradient-based Approach for Explaining Multimodal Deep Learning Classifiers 一种基于梯度的多模态深度学习分类器解释方法

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635460

Charles A. Ellis, Rongen Zhang, V. Calhoun, Darwin A. Carbajal, Robyn Miller, May D. Wang

{"title":"A Gradient-based Approach for Explaining Multimodal Deep Learning Classifiers","authors":"Charles A. Ellis, Rongen Zhang, V. Calhoun, Darwin A. Carbajal, Robyn Miller, May D. Wang","doi":"10.1109/BIBE52308.2021.9635460","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635460","url":null,"abstract":"In recent years, more biomedical studies have begun to use multimodal data to improve model performance. Many studies have used ablation for explainability, which requires the modification of input data. This can create out-of-distribution samples and lead to incorrect explanations. To avoid this problem, we propose using a gradient-based feature attribution approach, called layer-wise relevance propagation (LRP), to explain the importance of modalities both locally and globally for the first time. We demonstrate the feasibility of the approach with sleep stage classification as our use-case and train a 1-D convolutional neural network with electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) data. We also analyze the relationship of our local explainability results with clinical and demographic variables to determine whether they affect our classifier. Across all samples, EEG is the most important modality, followed by EOG and EMG. For individual sleep stages, EEG and EOG have higher relevance for awake and non-rapid eye movement 1 (NREM1). EOG is most important for REM, and EEG is most relevant for NREM2-NREM3. Also, LRP gives consistent levels of importance to each modality for the correctly classified samples across folds but inconsistent levels of importance for incorrectly classified samples. Our statistical analyses suggest that medication has a significant effect upon patterns learned for EEG and EOG NREM2 and that subject sex and age significantly affects the EEG and EOG patterns learned, respectively. Our results demonstrate the viability of gradient-based approaches for explaining multimodal electrophysiology classifiers and suggest their generalizability for other multimodal classification domains.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"222 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":"122884896","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: 7

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

A novel Greedy approach for Sequence based Computational prediction of Binding-Sites in Protein-Protein Interaction 一种基于序列的蛋白质相互作用结合位点计算预测的贪心方法

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635163

Aishwarya Purohit, S. Acharya, James Green

引用次数: 0

Automatic Curvature Analysis for Finely Interpolated Spinal Curves 精细插值脊柱曲线的自动曲率分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635424

M. Neghina, R. Petruse, S. Ćuković, Caliri Schiau, Nenad Filipović

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

Computational Finite Element Analysis of Aortic Root with Bicuspid Valve 带二尖瓣主动脉根部的计算有限元分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635269

Smiljana Tomasevic, I. Šaveljić, L. Velicki, N. Filipovic

{"title":"Computational Finite Element Analysis of Aortic Root with Bicuspid Valve","authors":"Smiljana Tomasevic, I. Šaveljić, L. Velicki, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635269","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635269","url":null,"abstract":"The aim of this work was to evaluate the impact of Bicuspid Aortic Valve (BAV), on displacements, Von Mises stress, shear stress and pressure distribution within the aortic root by using computational Finite Element (FE) method. The three-dimensional (3D) patient-specific geometry of dilated aortic root with BAV was reconstructed based on Computed Tomography (CT) scan images, in order to obtain the 3D finite element mesh. Two types of analyses: i) structural analysis and ii) computational fluid dynamics (CFD) were performed, with applied equivalent material characteristics of BAV and boundary conditions. The initial results for this single case, displacements and Von Mises stress distribution (for structural analysis), as well as shear stress and pressure distribution (for CFD analysis) were quantified concerning anatomical patient's structures. The regions of abnormal stresses on the aortic leaflets and annulus, with asymmetrically open bicuspid valve, were related to the increased pressures and shear stresses and analyzed for this patient-specific case. Due to the difficulties in obtaining such characteristics in vitro or in vivo, the performed computational analysis gave better insight into the biomechanics of the aortic root with BAV that is needed to achieve improvements in surgical repair techniques and presurgical planning.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"95 3 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":"116495222","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

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

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