Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003最新文献

The GeneCards/spl trade/ family of databases: GeneCards, GeneLoc, GeneNote and GeneAnnot GeneCards/spl交易/数据库家族:GeneCards, GeneLoc, GeneNote和GeneAnnot

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-09-08 DOI: 10.1109/CSB.2003.1227357

M. Safran, V. Chalifa-Caspi, O. Shmueli, Naomi Rosen, H. Benjamin-Rodrig, R. Ophir, I. Yanai, M. Shmoish, D. Lancet

引用次数: 2

Wavelet transforms for the analysis of microarray experiments 小波变换用于微阵列实验分析

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227358

T. Tokuyasu, D. Albertson, D. Pinkel, Ajay N. Jain

引用次数: 10

A pattern matching algorithm for codon optimization and CpG motif-engineering in DNA expression vectors DNA表达载体中密码子优化和CpG基序工程的模式匹配算法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227330

R. Satya, A. Mukherjee, U. Ranga

引用次数: 23

A contradiction-based framework for testing gene regulation hypotheses 基于矛盾的基因调控假设测试框架

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227430

S. Racunas, N. Shah, N. Fedoroff

引用次数: 5

An SVM-based algorithm for identification of photosynthesis-specific genome features 基于支持向量机的光合作用特异性基因组特征识别算法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227323

Gong-Xin Yu, G. Ostrouchov, A. Geist, N. Samatova

引用次数: 41

A computational method for assessing peptide-identification reliability in tandem mass spectrometry analysis with SEQUEST 一种评估串联质谱分析中肽鉴定可靠性的计算方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227353

J. Razumovskaya, V. Olman, Dong Xu, E. Uberbacher, N. Verberkmoes, Ying Xu

{"title":"A computational method for assessing peptide-identification reliability in tandem mass spectrometry analysis with SEQUEST","authors":"J. Razumovskaya, V. Olman, Dong Xu, E. Uberbacher, N. Verberkmoes, Ying Xu","doi":"10.1109/CSB.2003.1227353","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227353","url":null,"abstract":"High throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides using SEQUEST and then by combining the peptide hits for protein identification. Peptide identification is typically carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from the false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for \"normalizing\" SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128751489","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: 56

Integrating bioinformatics advances into disease management systems to improve quality of care 将生物信息学进展整合到疾病管理系统以提高护理质量

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227297

Stephen T. C. Wong

引用次数: 0

Development of a massively-parallel, biological circuit simulator 大规模并行生物电路模拟器的研制

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227426

R. Schiek, E. May

{"title":"Development of a massively-parallel, biological circuit simulator","authors":"R. Schiek, E. May","doi":"10.1109/CSB.2003.1227426","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227426","url":null,"abstract":"Genetic expression and control pathways can be successfully modeled as electrical circuits. Given the vast quantity of genomic data, very large and complex genetic circuits can be constructed. To tackle such problems, the massively-parallel, electronic circuit simulator, Xyce/sup /spl trade//, is being adapted to address biological problems. Unique to this biocircuit simulator is the ability to simulate not just one or a set of genetic circuits in a cell, but many cells and their internal circuits interacting through a common environment. Currently, electric circuit analogs for common biological and chemical machinery have been created. Using such analogs, one can construct expression, regulation and reaction networks. Individual species can be connected to other networks or cells via nondiffusive or diffusive channels (i.e. regions where species diffusion limits mass transport). Within any cell, a hierarchy of networks may exist operating at different time-scales to represent different aspects of cellular processes. Though under development, this simulator can model interesting biological and chemical systems. Prokaryotic genetic and metabolic regulatory circuits have been constructed and their interactions simulated for Escherichia coli's tryptophan biosynthesis pathway. Additionally, groups of cells each containing an internal reaction network and communicating via a diffusion limited environment can produce periodic concentration waves. Thus, this biological circuit simulator has the potential to explore large, complex systems and environmentally coupled problems.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"406 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126682159","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: 9

Spectral decomposition of the Laplacian matrix applied to RNA folding prediction 拉普拉斯矩阵的光谱分解应用于RNA折叠预测

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227419

D. Barash

{"title":"Spectral decomposition of the Laplacian matrix applied to RNA folding prediction","authors":"D. Barash","doi":"10.1109/CSB.2003.1227419","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227419","url":null,"abstract":"RNA secondary structure consists of elements such as stems, bulges, loops. The most obvious and important scalar number that can be attached to an RNA structure is its free energy, with a landscape that governs the folding pathway. However, because of the unique geometry of RNA secondary structure, another interesting single-signed scalar number based on geometrical scales exists that can assist in RNA structure computations. This scalar number is the second eigenvalue of the Laplacian matrix corresponding to a tree-graph representation of the RNA secondary structure. Because of the mathematical properties of the Laplacian matrix, the first eigenvalue is always zero, and the second eigenvalue (often denoted as the Fiedler eigenvalue) is a measure of the compactness of the associated tree-graph. The concept of using the Fiedler eigenvalue/eigenvector is borrowed from domain decomposition in parallel computing. Thus, along with the free energy, the Fiedler eigenvalue can be used as a signature in a clever search among a collection of structures by providing a similarity measure between RNA secondary structures. This can also be used for mutation predictions, classification of RNA secondary folds, filtering and clustering. Furthermore, the Fiedler eigenvector may be used to chop large RNAs into smaller fragments by using spectral graph partitioning, based on the geometry of the secondary structure. Each fragment may then be treated differently for the folding prediction of the entire domain.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126990338","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

Using Easel for modeling and simulating the interactions of cells in order to better understand the basics of biological processes and to predict their likely behaviors 使用Easel建模和模拟细胞的相互作用，以便更好地了解生物过程的基础知识，并预测它们可能的行为

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227392

V. Stojkovic, G. Steele, William L. Lupton

引用次数: 2