2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第2页

Numerical modeling of behavior of cancer cells after electroporation 电穿孔后癌细胞行为的数值模拟

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367723

T. Djukić, D. Cvetković, Milos D. Radovic, M. Zivanovic, N. Filipovic

引用次数: 1

KINOPTIM: The medical business intelligence module for fall prevention of the elderly KINOPTIM:老年人预防跌倒的医疗商业智能模块

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367637

Ioannis N. Kouris, C. Tsirbas, T. Tagaris, E. Vellidou, P. Vartholomeos, Stamatia Rizou, D. Koutsouris

引用次数: 3

Human Epithelial Type 2 cell classification with convolutional neural networks 基于卷积神经网络的人上皮2型细胞分类

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367705

N. Bayramoglu, Juho Kannala, J. Heikkilä

引用次数: 41

Improving sleep stage classification from electroencephalographic signals by fusion of contextual information 基于上下文信息融合的脑电信号睡眠阶段分类改进

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367736

I. Mporas, Anastasia Efstathiou, V. Megalooikonomou

引用次数: 1

Modeling the metabolism of escherichia coli under oxygen gradients with dynamically changing flux bounds 模拟大肠杆菌在动态变化通量边界的氧梯度下的代谢

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367691

J. Wulffen, Patrick C. F. Buchholz, O. Sawodny, R. Feuer

{"title":"Modeling the metabolism of escherichia coli under oxygen gradients with dynamically changing flux bounds","authors":"J. Wulffen, Patrick C. F. Buchholz, O. Sawodny, R. Feuer","doi":"10.1109/BIBE.2015.7367691","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367691","url":null,"abstract":"In bioindustrial large scale fermenters microorganisms are exposed to conditions of unsteady nutrient supply which occur only rarely on small lab-scale fermenters and lead to economic losses. In aerobic processes cells face different availabilities of oxygen, nitrogen and carbon sources along various directions inside a fermenter. The adaptation of the central metabolism in the facultative anaerobic bacterium Escherichia coli to changing oxygen concentrations will be investigated. Flux balance analysis (FBA) is an often used method to calculate reaction fluxes under given environmental conditions. FBA is based on a stoichiometric model with possible reaction fluxes which are limited by constraints. One sort of constraints are the lower and upper flux bounds. Existing methods of FBA describe metabolic adaptations to changing environments not in sufficient detail. This work develops a variant of FBA in order to close this gap. Balance equations for important gene transcripts and gene products are formulated and flux bounds are calculated continuously. The described variant of FBA is applied to a model of E. coli central metabolism. A transition between anaerobic and aerobic environment is simulated. The results are compared with a conventional FBA approach and regulatory FBA (rFBA). The FBA method described in this study shows possible targets for experimental validation.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"77 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114096860","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

Automatic estimation of the optimal AR order for epilepsy analysis using EEG signals 利用脑电图信号自动估计癫痫分析的最佳AR顺序

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367709

Evangelia Pippa, I. Mporas, V. Megalooikonomou

引用次数: 5

Neural network based approach for predicting maximal wall shear stress in the artery 基于神经网络的动脉壁最大剪应力预测方法

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367713

M. Blagojevic, Milos D. Radovic, M. Radovic, N. Filipovic

引用次数: 1

Application of smoothed particle hydrodynamics in biomechanics: Advanced procedure for discretization of complex biological shapes into pseudo-particles 光滑粒子流体力学在生物力学中的应用:复杂生物形状离散成伪粒子的高级程序

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367652

M. Topalovic, M. Blagojevic, A. Nikolic, M. Zivkovic, N. Filipovic

引用次数: 3

Automated identification of chicken eimeria species from microscopic images 鸡艾美耳球虫显微图像的自动鉴定

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367686

M. A. Abdalla, H. Seker, Richard Jiang

{"title":"Automated identification of chicken eimeria species from microscopic images","authors":"M. A. Abdalla, H. Seker, Richard Jiang","doi":"10.1109/BIBE.2015.7367686","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367686","url":null,"abstract":"Eimeria is an internal animal parasite that causes serious diseases and animal death, and reduces animal productivities. Eimeria has more than one species for every single genus of animals. An early diagnosis of Eimeria infection is usually achieved by examining fecal microscopy images. As Eimeria oocysts vary in terms of shapes, sizes and textures, they can be detected by measuring differences in their shapes, sizes and textural features. As these differences can be driven by analyzing pixel information in microscopic images, this paper therefore presents pixel-based features rather than using the oocysts morphological characteristics. This approach is then applied for the diagnosis of seven different species of Eimeria in chickens as a case study. The pixel-based features are the mean of pixel values over columns and rows of oocyst image matrices in grey-scaled images. The features have been extracted after detecting the oocyst edges by using Moore-Neighbor Tracing Algorithm. For the classification phase, K-Nearest Neighbor classifier was utilized. For its statistical validation, a 5-fold cross validation was adapted and run for 100 times. This proposed approach has yielded an average accuracy of 82% ± 0.54% This is a promising result that is potentially expected to lead fully automated portable parasite detection system.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133882719","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: 4

A hierarchical neural network for predicting protein functions 预测蛋白质功能的层次神经网络

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2015-11-02 DOI: 10.1109/BIBE.2015.7367651

J. C. Nievola, E. Paraiso, A. Freitas

{"title":"A hierarchical neural network for predicting protein functions","authors":"J. C. Nievola, E. Paraiso, A. Freitas","doi":"10.1109/BIBE.2015.7367651","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367651","url":null,"abstract":"This paper introduces the use of a modified feedforward neural network to cope with the problem of predicting protein functions. Since this kind of classification task is inherently hierarchical, this work proposes the use of two different architectures for the modified feedforward neural network, both mimicking the hierarchical nature of the classes (protein functions) to be predicted. The first approach consists of four feed-forward neural networks in cascade, each one taking as input the classification obtained by the previous network, which means, the input to a network is the classes that could be assigned to the protein at the immediately higher (parent) level in the class hierarchy. The second approach is an extension of the first one, which also adds as input to each sub-network the attributes of the protein being classified. In both situations, it was used two kinds of feed-forward architectures: an Adaline network, which is composed of a single layer of adjustable weights, and a MLP (\"Multi-Layer Perceptron\"), composed by two layers of adjustable weights. Both approaches were compared with a baseline consisting of a single MLP that maps the input attributes to the classes of the lowest level in the hierarchy. The MLP was built with the input layer, plus one hidden layer and one output layer. The three approaches were compared on eight datasets, the first four involving the prediction of GPCR (G-Protein Coupled Receptor) functions and the second four datasets involving the prediction of enzymes functions. The results show that a big-bang hierarchical neural network, based on the MLP paradigm, using a top-down evaluation for new instances has better behavior in hierarchical problems, when compared to its flat version.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123355720","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