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

Gene selection for cancer classification using bootstrapped genetic algorithms and support vector machines 基于自举遗传算法和支持向量机的癌症分类基因选择

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227389

Xue-wen Chen

引用次数: 52

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

A personalized and automated dbSNP surveillance system 个性化、自动化dbSNP监控系统

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227312

Shuo Liu, Steve Lin, Mark Woon, T. Klein, R. Altman

引用次数: 1

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

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

Computational inference of regulatory pathways in microbes 微生物调控途径的计算推理

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227429

Z. Su, P. Dam, Xin Chen, V. Olman, Tao Jiang, B. Palenik, Ying Xu

{"title":"Computational inference of regulatory pathways in microbes","authors":"Z. Su, P. Dam, Xin Chen, V. Olman, Tao Jiang, B. Palenik, Ying Xu","doi":"10.1109/CSB.2003.1227429","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227429","url":null,"abstract":"We present a computational protocol for inference of regulatory and signaling pathways in a microbial cell, through mining \"high-throughput\" biological data of various types, literature search, and computer-assisted human inference. This protocol consists of four key components: (a) construction of template pathways for microbial genomes related to the target genome, which have either been thoroughly studied or had a significant amount of relevant experimental data, (b) inference of target pathways for the target genome, by combining the template pathway models and target genome-specific information, (c) assignment of genes of the target genome to each individual functional roles in the pathway model, and (d) validation and refinement of the pathway models using pathway-specific experimental data or other information. To demonstrate the effectiveness of this procedure, we have applied this computational protocol to the construction of the phosphorus assimilation pathways in synechococcus WH8102. In this paper, we present a model of the core component of this pathway and provide our justification in support of the predicted model.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"17 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":"115132680","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

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

Aligning ESTs to genome using multilayer unique makers 利用多层独特标记将est与基因组对齐

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227409

F. Hsu, J. F. Chen

引用次数: 8

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