2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)最新文献_第3页

Reducing gene regulatory networks by decomposition 通过分解减少基因调控网络

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.117

Luonan Chen, Ruiqi Wang, K. Aihara

引用次数: 0

Volume measurements in the embryonic zebrafish heart using 4D confocal microscopy 使用4D共聚焦显微镜测量胚胎斑马鱼心脏的体积

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.141

M. Liebling, S. Fraser, M. Dickinson, A. Forouhar, M. Gharib

引用次数: 0

Rule clustering and super-rule generation for transmembrane segments prediction 跨膜段预测的规则聚类和超级规则生成

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.121

Jieyue He, Bernard Chen, Hae-Jin Hu, R. Harrison, P. Tai, Yisheng Dong, Yi Pan

引用次数: 8

Efficient 3D binary image skeletonization 高效的三维二值图像骨架化

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.57

Son T. Tran, L. Shih

{"title":"Efficient 3D binary image skeletonization","authors":"Son T. Tran, L. Shih","doi":"10.1109/CSBW.2005.57","DOIUrl":"https://doi.org/10.1109/CSBW.2005.57","url":null,"abstract":"Image skeletonization promises to be a powerful complexity-cutting tool for compact shape description, pattern recognition, robot vision, animation, petrography pore space fluid flow analysis, model/analysis of bone/lung/circulation, and image compression for telemedicine. The existing image Skeletonization techniques using boundary erosion, distance coding, and Voronoi diagram are first overviewed to assess/compare their feasibility of extending from 2D to 3D. An efficient distance-based procedure to generate the skeleton of large, complex 3D images such as CT, MRI data of human organ is then described. The proposed 3D Voxel Coding (3DVC) algorithm, is based on Discrete Euclidean Distance Transform. Instead of actual distance, each interior voxel (3D pixel) in the 3D image object is labeled with an integer code according to its relative distance from the object border for computation efficiency. All center voxels, which are the furthest away from the object border, are then collected and thinned to form clusters. To preserve the topology of the 3D image object, a cluster-labeling heuristic is then applied to order the clusters, and to recursively connect the next nearest clusters, gradually reducing the total number of disjoint clusters, to generate one final connected skeleton for each 3D object. The algorithm provides a straightforward computation which is robust and not sensitive to noise or object boundary complexity. Because 3D skeleton may not be unique, several application-dependent skeletonization options will be explored for meeting specific quality/speed requirements, and perhaps to incorporate automatic machine intelligence decisions. Parallel version of 3DVC is also introduced to further enhance skeletonization speed.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128095873","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: 32

Registering Drosophila embryos at cellular resolution to build a quantitative 3D atlas of gene expression patterns and morphology 以细胞分辨率登记果蝇胚胎，建立基因表达模式和形态的定量3D图谱

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.118

Charless C. Fowlkes, Jitendra Malik, C. L. Hendriks, S. Keränen, M. Biggin, D. W. Knowles, D. Sudar

引用次数: 11

Predicting continuous epitopes in proteins 预测蛋白质中的连续表位

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.109

Reeti Tandon, S. Adak, B. Sarachan, W. FitzHugh, Jeremy Heil, Vaibhav A. Narayan

引用次数: 0

Fractal clustering for microarray data analysis 微阵列数据分析的分形聚类

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.66

Lu-Yong Wang, A. Balasubramanian, A. Chakraborty, D. Comaniciu

{"title":"Fractal clustering for microarray data analysis","authors":"Lu-Yong Wang, A. Balasubramanian, A. Chakraborty, D. Comaniciu","doi":"10.1109/CSBW.2005.66","DOIUrl":"https://doi.org/10.1109/CSBW.2005.66","url":null,"abstract":"DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. Some clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or distance metric to cluster the points in multi-dimension linear Euclidean space. Poor consistence with the functional annotation of genes is shown in their validation study. A fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry is proposed. Fractal dimension is used to characterize the degree of self similarity among the points in the clusters. The main idea of fractal clustering is to group points in a cluster in such a way that none of the points in the cluster changes the cluster's intrinsic dimension radically. Hausdorff fractal dimension is computed through the means of the box-counting plot algorithm, since it is the fastest and also robust enough. This method is assessed using validation assessment using public microarray dataset. It shows that this method is superior in identifying functional related gene groups than other traditional methods.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"658 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116094892","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

Classification methods for HIV-1 medicated neuronal damage HIV-1药物性神经元损伤的分类方法

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.37

Men-Zhao Wang, Jialin C. Zheng, Zhengxin Chen, Yong Shi

{"title":"Classification methods for HIV-1 medicated neuronal damage","authors":"Men-Zhao Wang, Jialin C. Zheng, Zhengxin Chen, Yong Shi","doi":"10.1109/CSBW.2005.37","DOIUrl":"https://doi.org/10.1109/CSBW.2005.37","url":null,"abstract":"HIV-1-associated dementia (HAD) is the most devastating disease happened in the central nervous system of AIDS patients. Neuronal damage, the early indicator of HAD, under different treatments can be applied to design and study specific therapies for the prevention or reversal of the neuronal death associated with HAD. A computer-based image program was used to quantitatively estimate the change of neurites, arbors, branch nodes, and cell bodies in cultured cortical neurons. Nine attributes (variables) and two classes G2 (non-treatment control group) and G4 (gp120-treatment group) were considered to describe the statuses of neuronal damage. Various classification methods have been carried out in our research group. In this paper, we focus on using logistic regression method for classification, and compare the resulting predictive accuracy with that of using two-class multiple criteria linear programming (MCLP) and neural networks (NN) models conducted earlier. The results show that logistic regression obtained the best classification accuracy. As a pilot study, it demonstrates the use and effectiveness of statistical method in the classification mining of neuronal damage associated with HAD.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124306828","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 storage, processing, and retrieval system for microtubule tracking data 微管跟踪数据的存储、处理和检索系统

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.16

Robert John Coulier

引用次数: 1

Simulated pharmacogenomics exercises for the Cybertory/spl trade/ Virtual Molecular Biology Laboratory Cybertory/spl贸易/虚拟分子生物学实验室的模拟药物基因组学练习

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.126

C. Troup, Bill Martin, Carl E. McMillin, R. Horton

引用次数: 0