2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第4页

Cost and Time Prediction for Efficient Execution of Bioinformatics Workflows in Federated Cloud 联邦云中生物信息学工作流高效执行的成本和时间预测

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621199

M. Rosa, Aleteia P. F. Araujo, Felipe L. S. Mendes

{"title":"Cost and Time Prediction for Efficient Execution of Bioinformatics Workflows in Federated Cloud","authors":"M. Rosa, Aleteia P. F. Araujo, Felipe L. S. Mendes","doi":"10.1109/BIBM.2018.8621199","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621199","url":null,"abstract":"Cloud computing has devised an interesting computational model which provides a set of features such as storage, database and processing power, all made available as services. Recently, the concept of cloud computing has been extended to federated cloud computing in which different providers interconnect to provide more resources in an integrated and transparent way to the end user. Thus, the use of cloud platforms has been widely encouraged in applications that require a lot of processing and/or storage power, such as workflows in Bioinformatics. Users who operate such workflows are faced with a very large variety and amount of available resources, making it difficult to choose the correct ones for a certain workflow. This measurement is far from trivial and, in order to address this problem, this paper proposes an approach called sPCR (Cost Prediction and Computational Resources Service), which mixes GRASP metaheuristics and the multiple linear regression method with the purpose of dimensioning the resources to the users in a transparent way. In addition, sPCR allows the user to interact and choose between high-performance, low-budget runs, or set how much to pay and how long to finish workflows, all automatically and transparently. The results show that sPCR is able to efficiently estimate the resources, costs and execution time of workflows.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116219014","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: 8

BIBM 2018 Author Index BIBM 2018作者索引

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/bibm.2018.8621372

引用次数: 0

Efficient and Private Set Intersection of Human Genomes 人类基因组的高效私有集交叉

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621291

Liyan Shen, Xiaojun Chen, Dakui Wang, Binxing Fang, Ye Dong

引用次数: 8

An Adaptive Ray-Shooting Model for Terminations Detection: Applications in Neuron and Retinal Blood Vessel Images 一种用于终止检测的自适应射线射击模型:在神经元和视网膜血管图像中的应用

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621244

Weixun Chen, Min Liu, Ke-Qun Liu, Zhigang Ling

{"title":"An Adaptive Ray-Shooting Model for Terminations Detection: Applications in Neuron and Retinal Blood Vessel Images","authors":"Weixun Chen, Min Liu, Ke-Qun Liu, Zhigang Ling","doi":"10.1109/BIBM.2018.8621244","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621244","url":null,"abstract":"2D and 3D termination points are very good seeding point choices for the tree-like structure reconstruction in neuron or retinal blood vessel images. Previously, a ray-shooting model was proposed to detect the termination points in fluorescence microscopy images of neurons, by analyzing the pixel intensity distribution of the neighborhood around the neuron termination candidates. However, the length of the shooting rays and the number of z-slices taken into account in the existing ray-shooting model are fixed empirical number. This ray-shooting model cannot handle the diameter variation of neuron branches. In this paper, we propose an adaptive ray-shooting model to detect the terminations of neurons or retinal blood vessels by changing the length of the shooting rays according to their local diameters. The local diameter is estimated by the Multistencils Fast Marching Method (MSFM) in combination with the Rayburst sampling algorithm. We train a support vector machine (SVM) classifier to classify the termination points and non-termination points, by using the pixel intensity distribution features extracted by the adaptive ray-shooting model. Compared with the previous work, the experimental results on multiple neuron datasets and retinal blood vessel datasets show that our method significantly improves the detection accuracy rate by about 10% in challenging datasets.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126270972","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

Deep Convolutional Autoencoder for EEG Noise Filtering 脑电信号噪声滤波的深度卷积自编码器

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621080

N. M. N. Leite, E. Pereira, E. Gurjão, L. Veloso

{"title":"Deep Convolutional Autoencoder for EEG Noise Filtering","authors":"N. M. N. Leite, E. Pereira, E. Gurjão, L. Veloso","doi":"10.1109/BIBM.2018.8621080","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621080","url":null,"abstract":"Electroencephalography (EEG) signals may be severely affected by noise originated from various sources due to their low amplitude nature, specially if they are collected from scalp sensors. Several methods have been proposed for EEG denoising in order to facilitate diagnosis and communication in brain-computer interfaces, but such algorithms often have high complexity. This work presents a denoising approach based on deep learning using a deep convolutional autoencoder, which should reduce the effort of projecting denoising filters. Experiments were performed using two types of noise, originated from eye blink and from jaw clenching. Performance was evaluated with peak signal-to-noise ratio (PSNR) and the results showed that all confidence intervals for the proposed approach were superior to those obtained by the baseline bandpass traditional filtering method. Best average PSNR results for eye blink were obtained for Cz channels with $(20.3pm 2.6)mathrm{d}mathrm{B}$ versus $(14.3pm 2.4)mathrm{d}mathrm{B}$. For jaw clenching, best average PSNR results were obtained for Fz channels with $(21.7pm 3.1)mathrm{d}mathrm{B}$ versus $(13.9pm 2.6)mathrm{d}mathrm{B}$. The proposed approach seems to open a promising scope of research for noise filtering in EEG.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128054651","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: 41

Modeling and application of aorta coarctation: support system for pre-operative decision 主动脉缩窄:术前决策支持系统的建模与应用

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621566

L. T. Gaudio, P. Veltri, G. Fragomeni

{"title":"Modeling and application of aorta coarctation: support system for pre-operative decision","authors":"L. T. Gaudio, P. Veltri, G. Fragomeni","doi":"10.1109/BIBM.2018.8621566","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621566","url":null,"abstract":"Decision Support System (DSS) improves health care through the support given to health professionals in the task of decision making. Numerous uses of DSS are done in the context of heart failure. In congenital heart disease, aorta coarctation (CoA) is one of the most common lesions, and represents a spectrum of aortic narrowing that can vary from a discrete narrowing to a severe one. To this end, a 3D-model of the aorta was created by the patient’s CT-scan with the aim of improving the preoperative planning and a computation fluid dynamic (CFD) analysis was done to evaluate the results of the pre- and postsurgical hemodynamics. The main purpose of the following study is to provide an Application to support physicians during surgery. The application will enable to simulate an endovascular access and a stent-positioning procedure by varying both the position of the stent and the severity of the CoA, thus allowing to adapt the model to specific patient’s conditions. This will help the physician choose the intervention to be made by providing a quick support to consult in terms of variation of hemodynamic parameters before and after the surgery with the patient.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125643856","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: 3

Computational design of a legacy-based epitope vaccine against Human Cytomegalovirus 人巨细胞病毒遗留表位疫苗的计算设计

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621537

M. J. Quinzo, Esther M. Lafuente, P. Reche, D. Flower

{"title":"Computational design of a legacy-based epitope vaccine against Human Cytomegalovirus","authors":"M. J. Quinzo, Esther M. Lafuente, P. Reche, D. Flower","doi":"10.1109/BIBM.2018.8621537","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621537","url":null,"abstract":"Human Cytomegalovirus (HCMV) is a ubiquitous herpesvirus affecting approximately 90% of the world population. HCMV causes disease in immunologically-naive and immunosuppressed patient. The prevention, diagnosis and therapy of HCMV infection are thus crucial to public health. Despite the development of multiple prophylactic and pre-emptive therapeutic approaches, effective treatments remain a significant challenge. Thus, we sought to develop an epitope ensemble vaccine against HCMV by analyzing experimentally-defined HCMV-specific epitopes that effectively elicit B cell, CD4 T cell and CD8 T cell responses. The T cell component consists of 6 CD8 and 4 CD4 conserved T cell epitopes that were predicted to provide a population protection coverage over 90% and 80%. The B cell component consists of 2 B cell epitopes mapping onto glycoproteins L and H, respectively, which were selected by flexibility and solvent accessibility criteria. The proposed biological component makes this vaccine formulation not only multifunctional but also multi-antigenic, since it targets different early antigens that are vital for viral tropism, latency establishment, and replication. Here, we discuss the fundamental evidence supporting this approach and analyze its present limits","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121856321","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

Enrichment of SNOMED CT Ophthalmology Component to Support EHR Coding 支持EHR编码的SNOMED CT眼科组件

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621272

Hao Liu, P. L. Hildebrand, Y. Perl, J. Geller

引用次数: 1

Focal EEG signal detection based on constant-bandwidth TQWT filter-banks 基于恒带宽TQWT滤波器组的病灶脑电信号检测

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621311

Vipin Gupta, A. Nishad, R. B. Pachori

{"title":"Focal EEG signal detection based on constant-bandwidth TQWT filter-banks","authors":"Vipin Gupta, A. Nishad, R. B. Pachori","doi":"10.1109/BIBM.2018.8621311","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621311","url":null,"abstract":"Epilepsy is a neurological disease that identified by reoccurrence of seizures. The economic and commonly used method for the diagnosis of epilepsy is possible with the regular monitoring of electroencephalogram (EEG) signals. These EEG signals are complex in nature and the manual identification of these EEG signals is very much tedious task for the doctors. In this paper, a new methodology based on constant-bandwidth tunable-Q wavelet transform (TQWT) filter banks has been designed for the identification of medically not curable focal epilepsy EEG signals. In this proposed methodology, the non-focal and focal EEG signals are considered to extract sub-band signals by involving constant-bandwidth TQWT filter-banks. The mixture correntropy based features are obtained from sub-band signals of the EEG signals. The least squares support vector machine (LS-SVM) classifier along with radial basis function (RBF) kernel is used for the classification of these extracted features. The feature ranking methods are also used to reduce the features space. The achieved maximum classification accuracy in this proposed methodology is 90.01% using Bern-Barcelona EEG database.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"14 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451290","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: 14

Explainable Sentiment Analysis with Applications in Medicine 可解释情感分析在医学中的应用

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621359

C. Zucco, Huizhi Liang, G. D. Fatta, M. Cannataro

引用次数: 34