2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)最新文献_第6页

Predicting gene function by combining expression and interaction data 结合表达和相互作用数据预测基因功能

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.111

R. V. Berlo, L. Wessels, S. Martes, M. Reinders

引用次数: 3

PROMOCO: a new program for prediction of cis regulatory elements: from high-information content analysis to clique identification PROMOCO:一个预测顺式调控元素的新程序:从高信息含量分析到集团识别

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.113

Guojun Li, Jizhu Lu, V. Olman, Ying Xu

引用次数: 1

Cluster utility: a new metric for clustering biological sequences 聚类效用:生物序列聚类的新度量

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.38

Jason Lee, Sun Kim

引用次数: 2

Non-occurring and rare quads in PDB and translated introns from XPro with possible applications in nanostructure design PDB中不存在的和罕见的四元体以及XPro翻译的内含子在纳米结构设计中的可能应用

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.97

G. Sampath, James TenEyck

引用次数: 0

Open microscopy environment 开放显微镜环境

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.100

I. Goldberg

{"title":"Open microscopy environment","authors":"I. Goldberg","doi":"10.1109/CSBW.2005.100","DOIUrl":"https://doi.org/10.1109/CSBW.2005.100","url":null,"abstract":"Summary form only given. The Open Microscopy Environment (OME) is a framework for the management and analysis of image data and metadata in biological microscopy. Biological microscopy images can be collected in many different ways, and may represent many different kinds of information which changes continually with evolving technology and experimental goals. A framework that fully encompasses biological microscopy in scope cannot rely on a fixed data model - it must be designed to accommodate ever-changing informatics needs. The challenge posed by a fluid data model is similar to one addressed by the semantic web, principally support for arbitrary or user-defined semantics and ontologies. However, an analysis framework faces additional challenges besides the management of semantics: definition of analytic units as transforms between semantic constructs, definition of aggregates of analytic units to represent work flows, interfaces to algorithm implementations, and maintenance of data provenance or history. A collaborative scientific environment also demands that these locally defined semantics and transforms be fully transportable for subsequent review or analysis. OME incorporates these components in a database-backed system targeted at end-user biologists. This presentation will briefly describe the various components of OME: the semantic layer (Semantic Types - STs), the analytical un its used to transform between STs (Analysis Modules), the units of work flow (Analysis Chains), the work-flow processor (Analysis Engine), and the transportability layer (OME XML). An example of a complex work flow will also be presented illustrating how this system is used for automated image classification.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"7 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":"128507687","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: 26

A generic algorithm to find all common intervals of two permutations 寻找两个排列的所有公共区间的通用算法

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.9

G. Feng, Yujiang Shan

引用次数: 0

Novel hybrid hierarchical-K-means clustering method (H-K-means) for microarray analysis 微阵列分析的H-K-means混合聚类方法

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.98

Bernard Chen, R. Harrison, Yi Pan, P. Tai

{"title":"Novel hybrid hierarchical-K-means clustering method (H-K-means) for microarray analysis","authors":"Bernard Chen, R. Harrison, Yi Pan, P. Tai","doi":"10.1109/CSBW.2005.98","DOIUrl":"https://doi.org/10.1109/CSBW.2005.98","url":null,"abstract":"Hierarchical and k-means clustering are two major analytical tools for unsupervised microarray datasets. However, both have their innate disadvantages. Hierarchical clustering cannot represent distinct clusters with similar expression patterns. Also, as clusters grow in size, the actual expression patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses initial centroids randomly: in addition, it is sensitive to outliers. We present a novel hybrid approach to combined merits of the two and discard disadvantages we mentioned above. It is different from existed method: carry out hierarchical clustering first to decide location and number of clusters in the first round and run the K-means clustering in another round. The brief idea is we cluster around half data through hierarchical clustering and succeed by K-means for the rest half in one single round. Also, our approach provides a mechanism to handle outliers. Comparing with existed hybrid clustering approach and K-means clustering in 2 different distance measure on Eisen's yeast microarray data, our method always generate much higher quality clusters.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"48 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":"133767312","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: 97

Eigenphenotypes: towards an algorithmic framework for phenotype discovery 特征表型:对表现型发现的算法框架

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.60

Alexander Vaughan, Rahul Singh, Ilmi Yoon, M. Fuse

引用次数: 2

Mining protein sequence motifs representing common 3D structures 挖掘代表常见3D结构的蛋白质序列基序

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.93

Wei Zhong, Gulsah Altun, R. Harrison, P. Tai, Yi Pan

{"title":"Mining protein sequence motifs representing common 3D structures","authors":"Wei Zhong, Gulsah Altun, R. Harrison, P. Tai, Yi Pan","doi":"10.1109/CSBW.2005.93","DOIUrl":"https://doi.org/10.1109/CSBW.2005.93","url":null,"abstract":"Understanding the relationship between protein structure and its sequence is one of the most important tasks of current bioinformatics research. In this work, recurring protein sequence motifs are explored with a K-means clustering algorithm. No structural information is used during the clustering process so that the relationship between sequence similarity and structural similarity for sequence-based clusters can be studied. This work focuses on characterizing structural similarity so that the quality of sequence clusters can be assessed accurately. Analysis of results reveals that the combined metric of distance matrix root mean squared deviation for sequence cluster (dmRMSD/spl I.bar/SC) and torsion angle RMSD/spl I.bar/SC (taRMSD/spl I.bar/SC) can provide the reliable indication of structural similarity for sequence clusters. Based on our combined metric, the recurrent sequence clusters with high structural similarity are used to generate sequence motifs. The common 3D structure of a sequence motif is represented by both representative backbone torsion angles and average distance matrices of the sequence cluster used to produce this motif. These motifs provide the foundation to develop a protein vocabulary reflecting sequence-structure correspondence.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"14 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":"127948481","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

Computing MEG signal sources 计算MEG信号源

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.42

J. M. Eklund, R. Bajcsy, J. Sprinkle, G. V. Simpson

引用次数: 1