2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第2页

Tackling the challenging motif problem through hybrid particle swarm optimized alignment clustering 利用混合粒子群优化的排列聚类方法解决具有挑战性的基序问题

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948452

Chengpeng Bi

{"title":"Tackling the challenging motif problem through hybrid particle swarm optimized alignment clustering","authors":"Chengpeng Bi","doi":"10.1109/CIBCB.2011.5948452","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948452","url":null,"abstract":"Previous studies show that Gibbs sampling methods and the like desperately failed to solve the challenging motif problem. This paper proposes a new hybrid algorithm, integrated Gibbs with particle swarm optimization (PSO) based motif alignment clustering (PSO-MAC), to solve the challenging motif problem by iteratively refining a population of potential solutions. The PSO-MAC algorithm is closely incorporated into a variant Gibbs called pseudo-Gibbs (pGibbs) motif sampler. Notably, pGibbs as a forerunner is executed multiple times and hence it brings about a population of potential alignments. Then, a PSO procedure coupled with motif alignment clustering (MAC) is developed to fine-tune such a population of solutions. The hybrid PSO-MAC algorithm aims to glean high quality motif solutions by cyclically refining and clustering the solution pool. Simulation and experimental results show that the new hybrid algorithm performs markedly better than others tested, and surprisingly it is able to solve the challenging motif problem with high precision. The new hybrid algorithm is also successfully applied to large-scale ChIP-Seq data sets.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125192983","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

Improving the transparency in fuzzy modelling of radiotherapy margins in cancer treatment 提高肿瘤治疗放射界模糊建模的透明度

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948453

B. Mzenda, David J. Brown, A. Gegov

引用次数: 0

ROAR: A Reference Ontology for Anatomical Relations 吼叫:解剖关系的参考本体

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948470

Alton B. Coalter, J. Leopold

引用次数: 1

Optimizing the Salmon Algorithm for the construction of DNA error-correcting codes 优化鲑鱼算法构建DNA纠错码

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948476

John Orth, S. Houghten

引用次数: 8

Utilization of gene ontology in semi-supervised clustering 基因本体在半监督聚类中的应用

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948467

D. D. Doan, Yunli Wang, Youlian Pan

引用次数: 4

Secondary structure element voting for RNA gene finding RNA基因发现的二级结构元件投票

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948477

N. Erho, K. Wiese

引用次数: 2

Generalized operators and its application to a nonlinear fuzzy clustering model 广义算子及其在非线性模糊聚类模型中的应用

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948471

M. Sato-Ilic

引用次数: 0

Illumination field estimation through background detection in optical microscopy 光学显微镜中基于背景检测的照明场估计

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948457

A. Gherardi, A. Bevilacqua, F. Piccinini

{"title":"Illumination field estimation through background detection in optical microscopy","authors":"A. Gherardi, A. Bevilacqua, F. Piccinini","doi":"10.1109/CIBCB.2011.5948457","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948457","url":null,"abstract":"Automated microscopic image analysis techniques are increasingly gaining attention in the field of biological imaging. The success of these applications mostly depends on the earlier image processing steps applied to the acquired images, aiming at enhancing image content while performing noise and artifacts removal. One such artifact is the vignetting effect that in general occurs in most imaging sensors due to an uneven illumination of the scene being imaged. As a consequence, images are usually lighter near the optical center and darker at image borders. This effect is particularly evident when stitching images into a mosaic in order to increase the field of view of the microscope. The existing approaches deal with either the parametric model of the known light distribution or the estimation of the illumination field based on just one image or a sequence of empty-field images. These approaches are only feasible when the acquisition apparatus is at one's disposal. We propose a non parametric and general purpose approach, without using prior information about the light distribution, where the illumination field is estimated from the background, that is built automatically stemming from a sequence of images containing even the objects of interest.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121333198","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: 17

A comparison of Finite State Classifier and Mahalanobis-Taguchi System for multivariate pattern recognition in skin cancer detection 有限状态分类器与Mahalanobis-Taguchi系统在皮肤癌多变量模式识别中的比较

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948469

E. Cudney, S. Corns

引用次数: 7

Reverse engineering of gene regulatory networks: A systems approach 基因调控网络的逆向工程:系统方法

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948475

Zhen Wang, P. Mousavi

{"title":"Reverse engineering of gene regulatory networks: A systems approach","authors":"Zhen Wang, P. Mousavi","doi":"10.1109/CIBCB.2011.5948475","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948475","url":null,"abstract":"In the last decade many computational approaches have been introduced to model networks of molecular interactions from gene expression data. Such networks can provide an understanding of the regulatory mechanisms in the cells. System identification algorithms refer to a group of approaches that capture the dynamic relationship between the input and output of a system, and provide a deterministic model of its function. These approaches have been extensively developed for engineering systems, and have reasonable computational requirements. In this paper, we present two system identification methods applied to reverse engineering of gene regulatory networks. Gene regulatory networks are constructed as systems where the output to be estimated is an expression profile of a gene, and the inputs are the potential regulators of that gene. The first reverse engineering method is based on orthogonal search and selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of multiple cascade models; each cascade includes a dynamic component and a static component. Several cascades are used in parallel to reduce the difference of the estimated expression profiles with the actual ones. To assess the performance of the proposed methods, they are applied to a temporal synthetic dataset, a simulated gene expression time series of songbird brain, and yeast Saccharomyces Cerevisiae cell cycle. Results are compared to known mechanisms of the underlying data and the literature, and demonstrate that the proposed approaches capture the underlying interactions as networks.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124415936","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