Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003最新文献_第10页

Iterative rank based methods for clustering 基于迭代秩的聚类方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227379

S. Perrey, H. Brinck, A. Zielesny

引用次数: 1

Eulerian path methods for multiple sequence alignment 多序列比对的欧拉路径方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227296

M. Waterman, Yu Zhang

引用次数: 2

Probability profiles-novel approach in tandem mass spectrometry De Novo sequencing 概率剖面-串联质谱新方法De Novo测序

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227351

T. Fridman, Robert M. Day, Jane Razumovsbya, Dong Xu, A. Gorin

引用次数: 3

PTC: an interactive tool for phylogenetic tree construction 系统发育树构建的交互式工具

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227378

Chen Yang, S. Khuri

引用次数: 8

Statistical inference for well-ordered structures in nucleotide sequences 核苷酸序列中有序结构的统计推断

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227318

S. Le, Jih-Hsiang Chen, J. Maizel

{"title":"Statistical inference for well-ordered structures in nucleotide sequences","authors":"S. Le, Jih-Hsiang Chen, J. Maizel","doi":"10.1109/CSB.2003.1227318","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227318","url":null,"abstract":"Distinct, local structures are frequently correlated with functional RNA elements involved in post-transcriptional regulation of gene expression. Discovery of microRNAs (miRNAs) suggests that there are a large class of small noncoding RNAs in eukaryotic genomes. These miRNAs have the potential to form distinct fold-back stem-loop structures. The prediction of those well-ordered folding sequences (WFS) in genomic sequences is very helpful for our understanding of RNA-based gene regulation and the determination of local RNA elements with structure-dependent functions. In this study, we describe a novel method for discovering the local WFS in a nucleotide sequence by Monte Carlo simulation and RNA folding. In the approach the quality of a local WFS is assessed by the energy difference (E/sub diff/) between the optimal structure folded in the local segment and its corresponding optimal, restrained structure where all the previous base pairings formed in the optimal structure are prohibited. Distinct WFS can be discovered by scanning successive segments along a sequence for evaluating the difference between Ediff of the natural sequence and those computed from randomly shuffled sequences. Our results indicate that the statistically significant WFS detected in the genomic sequences of Caenorhabditis elegans (C.elegans) F49E12, T07C5, T07D1, T10H9, Y56A3A and Y71G12B are coincident with known fold-back stem-loops found in miRNA precursors. The potential and implications of our method in searching for miRNAs in genomes is discussed.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"214 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":"114593185","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: 11

A block coding method that leads to significantly lower entropy values for the proteins and coding sections of Haemophilus influenzae 一种块编码方法，可显著降低流感嗜血杆菌蛋白质和编码部分的熵值

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227329

G. Sampath

引用次数: 8

Genetic algorithm approach for the closest string problem 最近弦问题的遗传算法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227407

Holger Mauch, M. Melzer, John S. Hu

{"title":"Genetic algorithm approach for the closest string problem","authors":"Holger Mauch, M. Melzer, John S. Hu","doi":"10.1109/CSB.2003.1227407","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227407","url":null,"abstract":"A fundamental aspect of post-transcriptional gene silencing (PTGS) or RNA interference (RNAi) is the requirement of sequence homology between the transgene and viral or messenger RNAs being targeted. For example, virus-resistant transgenic plants are resistant only to viruses that are closely related (i.e. high sequence homology) to the virus from which the transgene was derived. One idea for broadening this resistance is to devise an artificial sequence that incorporates the sequence variation found in a viral population. This requires an algorithm which can determine an artificial sequence with an optimal (or at least a 90-95% ) homology to all of the viral sequences in a population. The genetic algorithm (GA) presented in this paper serves this purpose. It should be of great value to all researchers who utilize PTGS or RNAi. In the context of coding theory, the task is to find the radius of a code S /spl sub/ {A, C, G, T} /sup n/. In computational biology this problem is commonly referred to as the closest string problem. Experimental results suggest that this NP-complete optimization problem can be approached well with a custom-built GA.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"14 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":"124005400","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: 25

Molecular evaluation using comparative molecular interaction profile analysis system 利用比较分子相互作用谱分析系统进行分子评价

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227387

Yoshiharu Hayashi, Katsuyoshi Sakaguchi, Nao Iwata, Masaki Kobayashi

引用次数: 0

Gene selection for multiclass prediction of microarray data 微阵列数据多类别预测的基因选择

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227385

Dechang Chen, D. Hua, J. Reifman, Xiuzhen Cheng

{"title":"Gene selection for multiclass prediction of microarray data","authors":"Dechang Chen, D. Hua, J. Reifman, Xiuzhen Cheng","doi":"10.1109/CSB.2003.1227385","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227385","url":null,"abstract":"Gene expression data from microarrays have been successfully applied to class prediction, where the purpose is to classify and predict the diagnostic category of a sample by its gene expression profile. A typical microarray dataset consists of expression levels for a large number of genes on a relatively small number of samples. As a consequence, one basic and important question associated with class prediction is: how do we identify a small subset of informative genes contributing the most to the classification task? Many methods have been proposed but most focus on two-class problems, such as discrimination between normal and disease samples. This paper addresses selecting informative genes for multiclass prediction problems by jointly considering all the classes simultaneously. Our approach is based on the power of the genes in discriminating among the different classes (e.g., tumor types) and the existing correlation between genes. We formulate the expression levels of a given gene by a one-way analysis of variance model with heterogeneity of variances, and determine the discriminatory power of the gene by a test statistic designed to test the equality of the class means. In other words, the discriminatory power of a gene is associated with a Behrens-Fisher problem. Informative genes are chosen such that each selected gene has a high discriminatory power and the correlation between any pair of selected genes is low. Test statistics considered in this paper include the ANOVA F test statistic, the Brown-Forsythe test statistic, the Cochran test statistic, and the Welch test statistic. Their performances are evaluated over several classification methods applied to two publicly available microarray datasets. The results show that Brown-Forsythe test statistic achieves the best performance.","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":"124288880","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: 22

A computational approach to reconstructing gene regulatory networks 重构基因调控网络的计算方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227350

Xutao Deng, H. Ali

{"title":"A computational approach to reconstructing gene regulatory networks","authors":"Xutao Deng, H. Ali","doi":"10.1109/CSB.2003.1227350","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227350","url":null,"abstract":"With the rapid accumulation of gene expression data in publicly accessible databases, computational study of gene regulation has become an obtainable goal Intrinsic to this task will be data mining tools for inferring knowledge from biological data. In this project, we have developed a new data mining technique in which we adapt the connectivity of a recurrent neural network model by indexing regulatory elements and including nonlinear interaction terms. The new technique reduces the number of parameters by O(n), therefore increasing the chance of recovering the underlying regulatory network. In order to fit the model from data, we have developed a genetic fitting algorithm with O(n) time complexity and that adapts the connectivity during the fitting process until a satisfactory fit is obtained. We have implemented this fitting algorithm and applied it to two data sets: rat central nervous system development (CNS) data with 112 genes, and yeast whole genome data with 2467 genes. With multiple runs of the fitting algorithm, we were able to efficiently generate a statistical pattern of the model parameters from the data. Because of its adaptive features, this method will be especially useful for reconstructing coarse-grained gene regulatory network from large scale or genome scale gene expression data sets.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"35 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":"125012563","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: 10