2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第4页

Sensitivity Comparison Between Reflection and Transmission Coefficient by Free Space Method for Non-Invasive Glucose Monitoring Sensor 无创血糖监测传感器反射系数与透射系数的自由空间法灵敏度比较

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-25

Kento Fujimori, Ning Li, Y. Sugimoto

引用次数: 0

Comparative Finite Element Analysis of Patient-Specific Tricuspid and Bicuspid Aortic Valve 三尖瓣与二尖瓣主动脉瓣的有限元对比分析

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00013

S. Djorovic, N. Filipovic, A. Milosavljevic, L. Velicki

{"title":"Comparative Finite Element Analysis of Patient-Specific Tricuspid and Bicuspid Aortic Valve","authors":"S. Djorovic, N. Filipovic, A. Milosavljevic, L. Velicki","doi":"10.1109/BIBE.2017.00013","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00013","url":null,"abstract":"The main purpose of this study was to examine and compare the biomechanical characteristics of a healthy tricuspid aortic valve (TAV) and diseased bicuspid aortic valve (BAV). The patient-specific geometrical model of TAV was created based on computed tomography (CT) scan images. On the same model, two leaflets (left and right) were manually fused in order to create the BAV model (type 1). The finite element analysis was performed using the algorithms and numerical methods for structural analysis on computational meshes. Also, equivalent material characteristics and boundary conditions were applied. As the result, displacements and Von Mises stress distribution were computed concerning anatomical differences between TAV and BAV structures. In the case of TAV, leaflets were symmetrically and centrally open, while BAV analysis resulted with regions of increased stresses on the leaflets with elliptically open valve. The performed comparative computational analysis gave better insight into the biomechanics of healthy and malformed aortic root. It may contribute to monitoring of structural characteristics due to the difficulty of obtaining such characteristics in vitro or in vivo.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128427397","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: 1

Connectivity Dynamics of Interictal Epileptiform Activity 癫痫样活动间期的连通性动力学

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-17

H. Rajaei, M. Cabrerizo, Panuwat Janwattanapong, Alberto Pinzon-Ardila, S. Gonzalez-Arias, A. Barreto, M. Adjouadi

{"title":"Connectivity Dynamics of Interictal Epileptiform Activity","authors":"H. Rajaei, M. Cabrerizo, Panuwat Janwattanapong, Alberto Pinzon-Ardila, S. Gonzalez-Arias, A. Barreto, M. Adjouadi","doi":"10.1109/BIBE.2017.00-17","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-17","url":null,"abstract":"Patterns of interictal epileptiform activities, such as sharp waves, spikes, spike-wave complexes and polyspike-wave complexes are explored in the recorded electroencephalograms (EEG) to gauge the different functional connectivity dynamics and to assess how they could be affected by the type of a seizure. Connectivity measures were represented by the phase synchronization among scalp electrodes that were obtained by adopting a nonlinear data-driven method. These interictal epileptic activities were investigated using a graph theory analysis. The connectivity maps were compared by considering the number of connections in four main brain regions (anterior region, posterior region, left hemisphere and right hemisphere). Results revealed interesting and different network topology for the connectivity maps. Besides, a relationship between the connectivity patterns of the recorded epileptic activities and the types of seizures was observed. This relationship was statistically confirmed by analysis of variance (ANOVA) that denoted a significant difference among connectivity patterns of sharp waves and spike activities, which were seen in focal epilepsy, in contrast to the spike-wave and polyspike-wave complexes that were associated with generalized epilepsy (P-value = 0). These results augment the prospects for diagnosis and enhance the recognition of the disease type via EEG-based connectivity maps.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114491310","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: 6

Latent Dirichlet Allocation for Classification using Gene Expression Data 利用基因表达数据进行分类的潜在狄利克雷分配

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-81

H. Yalamanchili, S. Kho, M. Raymer

{"title":"Latent Dirichlet Allocation for Classification using Gene Expression Data","authors":"H. Yalamanchili, S. Kho, M. Raymer","doi":"10.1109/BIBE.2017.00-81","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-81","url":null,"abstract":"Understanding the role of differential gene expression in the development of, and molecular response to, cancer is a complex problem that remains challenging, in part due to the sheer number of genes, gene products, and metabolites involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to explore patterns of gene expression in healthy and cancer tissues. An important advantage of LDA compared to alternative statistical and machine learning methods is its proven ability to handle sparse inputs over an extremely large numbers of features in an unsupervised manner. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. In this paper, we try to optimize the protocol and parameters for efficient implementation of LDA. Here, messenger RNA (mRNA) sequence data from breast cancer and healthy tissue is used to determine an effective approach for the application of LDA to classification of cancer versus healthy tissue. We describe our study in two phases: First, various parameters like the number of topics, bins and passes were optimized for LDA. Next we developed a novel LDA-based classification approach to classify unknown samples based on similarity of co-expression patterns. Evaluation to assess the effectiveness of this approach shows that LDA can achieve high accuracy compared to alternative approaches. Overall, our results project LDA as a promising approach for classification of tissue types based on gene expression data in cancer studies.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125387548","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: 9

CALI: A Novel Visual Model for Frequent Pattern Mining in Protein-Ligand Graphs CALI:蛋白质配体图中频繁模式挖掘的一种新的可视化模型

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-29

G. SusanaMedina, A. V. Fassio, S. Silveira, C. H. Silveira, R. Minardi

引用次数: 2

Modeling Global and local Codon Bias with Deep Language Models 用深度语言模型建模全局和局部密码子偏差

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-63

M. Fujimoto, P. Bodily, Cole A. Lyman, Andrew J. Jacobsen, Q. Snell, M. Clement

引用次数: 3

Holographic Interface for three-dimensional Visualization of MRI on HoloLens: A Prototype Platform for MRI Guided Neurosurgeries HoloLens上MRI三维可视化的全息界面:MRI引导神经外科手术的原型平台

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-84

C. M. M. Mojica, N. Navkar, N. Tsekos, Dimitrios Tsagkaris, A. Webb, T. Birbilis, I. Seimenis

引用次数: 22

Gamification in Social Networking: A Platform for People Living with Dementia and their Caregivers 社交网络中的游戏化:痴呆症患者及其护理人员的平台

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00015

I. Paliokas, A. Tzallas, N. Katertsidis, K. Votis, D. Tzovaras

引用次数: 7

Multi-Task Learning for Commercial Brain Computer Interfaces 商用脑机接口的多任务学习

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-73

G. Panagopoulos

{"title":"Multi-Task Learning for Commercial Brain Computer Interfaces","authors":"G. Panagopoulos","doi":"10.1109/BIBE.2017.00-73","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-73","url":null,"abstract":"In the field of Brain Computer Interfaces, one of the most crucial hindrances towards everyday applicability is the problem of subject-to-subject generalization. This adheres to the fact that neural signals vary significantly across subjects, because of the inherent person specific variability, rendering a subject calibration process necessary for the pattern recognition mechanisms of a BCI to achieve a notable performance. In the present work, we explore this phenomenon on two open datasets from mental monitoring experiments which utilized a commercial BCI device (Neurosky). This passive BCI setting with economical hardware is one of the must promising in terms of commercial appeal and hence it has more potential to be employed by multiple subjects-users. We visualize the so-called inter subject variability problem and apply machine learning methods commonly used in BCI literature. Subsequently we employ multi-task learning algorithms, setting each subject specific classification as a separate task. The experiments reveal that multi-task approaches achieve better accuracy with increasing number of subjects in contrast to conventional models, while providing insights that are consistent among subjects and agree with the relevant literature.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132711687","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: 9

3D Segmentation,Visualization and Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells using Time-Lapse Fluorescence Microscopy Images 利用延时荧光显微镜图像对小鼠胚胎干细胞分化活性进行三维分割、可视化和定量分析

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-65

Yuan-Hsiang Chang, H. Yokota, K. Abe, C. Chen, Ming-Dar Tsai

{"title":"3D Segmentation,Visualization and Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells using Time-Lapse Fluorescence Microscopy Images","authors":"Yuan-Hsiang Chang, H. Yokota, K. Abe, C. Chen, Ming-Dar Tsai","doi":"10.1109/BIBE.2017.00-65","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-65","url":null,"abstract":"This paper explores the feasibility of automatic 3D segmentation, visualization and quantitative analysis for differentiation activities of mouse embryonic stem cells using time-lapse confocal fluorescence microscopy images. Technical approaches include bilateral filtering, mean-shift segmentation, adaptive thresholding, watershed segmentation, connected component labeling, and video tracking. Our method processes simultaneously two image channels, one for cytoplasm and the other for nuclei. The nucleus images are used to segment 2D and then 3D nuclei and to track each nucleus and calculate velocities of the 3D nucleus. The cytoplasm images are used to help nucleus segmentation and calculate the S/V (surface to volume) ratio of cytoplasm surrounding a nucleus. Volume rendering on the time-lapse fluorescence images generates time-series 3D images for visualizing the dynamic changes of cell velocity and S/V ratios. Using our prototype system, cells with different amount of EGFP fluorescent protein possesses different differentiation activity (velocity and S/V ratio) can be visualized and quantitatively analyzed.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132925228","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: 1