Proceedings. IEEE Computer Society Bioinformatics Conference最新文献_第2页

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。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Gong-Xin Yu, George Ostrouchov, Al Geist, Nagiza F Samatova

引用次数: 0

cWINNOWER algorithm for finding fuzzy DNA motifs. cWINNOWER算法寻找模糊DNA基序。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Shoudan Liang

引用次数: 0

Keynote address: the role of algorithmic research in computational genomics. 主题演讲:算法研究在计算基因组学中的作用。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Richard M Karp

引用次数: 0

Can we identify cellular pathways implicated in cancer using gene expression data? 我们能否利用基因表达数据识别与癌症相关的细胞通路?

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Nigam Shah, Jorge Lepre, Yuhai Tu, Gustavo Stolovitzky

引用次数: 0

Fourier harmonic approach for visualizing temporal patterns of gene expression data. 可视化基因表达数据时间模式的傅立叶调和方法。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Li Zhang, Aidong Zhang, Murali Ramanathan

引用次数: 0

Group testing with DNA chips: generating designs and decoding experiments. DNA芯片组测:生成设计和解码实验。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Alexander Schliep, David C Torney, Sven Rahmann

{"title":"Group testing with DNA chips: generating designs and decoding experiments.","authors":"Alexander Schliep, David C Torney, Sven Rahmann","doi":"","DOIUrl":"","url":null,"abstract":"DNA microarrays are a valuable tool for massively parallel DNA-DNA hybridization experiments. Currently, most applications rely on the existence of sequence-specific oligonucleotide probes. In large families of closely related target sequences, such as different virus subtypes, the high degree of similarity often makes it impossible to find a unique probe for every target. Fortunately, this is unnecessary. We propose a microarray design methodology based on a group testing approach. While probes might bind to multiple targets simultaneously, a properly chosen probe set can still unambiguously distinguish the presence of one target set from the presence of a different target set. Our method is the first one that explicitly takes cross-hybridization and experimental errors into account while accommodating several targets. The approach consists of three steps: (1) Pre-selection of probe candidates, (2) Generation of a suitable group testing design, and (3) Decoding of hybridization results to infer presence or absence of individual targets. Our results show that this approach is very promising, even for challenging data sets and experimental error rates of up to 5%. On a data set of 28S rDNA sequences we were able to identify 660 sequences, a substantial improvement over a prior approach using unique probes which only identified 408 sequences.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"2 ","pages":"84-91"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25834436","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

Haplotype motifs: an algorithmic approach to locating evolutionarily conserved patterns in haploid sequences. 单倍型基序:一种在单倍体序列中定位进化保守模式的算法方法。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Russell Schwartz

{"title":"Haplotype motifs: an algorithmic approach to locating evolutionarily conserved patterns in haploid sequences.","authors":"Russell Schwartz","doi":"","DOIUrl":"","url":null,"abstract":"The promise of plentiful data on common human genetic variations has given hope that we will be able to uncover genetic factors behind common diseases that have proven difficult to locate by prior methods. Much recent interest in this problem has focused on using haplotypes (contiguous regions of correlated genetic variations), instead of the isolated variations, in order to reduce the size of the statistical analysis problem. In order to most effectively use such variation data, we will need a better understanding of haplotype structure, including both the general principles underlying haplotype structure in the human population and the specific structures found in particular genetic regions or sub-populations. This paper presents a probabilistic model for analyzing haplotype structure in a population using conserved motifs found in statistically significant sub-populations. It describes the model and computational methods for deriving the predicted motif set and haplotype structure for a population. It further presents results on simulated data, in order to validate the method, and on two real datasets from the literature, in order to illustrate its practical application.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"2 ","pages":"306-14"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25834893","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

CTSS: a robust and efficient method for protein structure alignment based on local geometrical and biological features. CTSS:一种基于局部几何和生物特征的蛋白质结构定位方法。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Tolga Can, Yuan-Fang Wang

{"title":"CTSS: a robust and efficient method for protein structure alignment based on local geometrical and biological features.","authors":"Tolga Can, Yuan-Fang Wang","doi":"","DOIUrl":"","url":null,"abstract":"We present a new method for conducting protein structure similarity searches, which improves on the accuracy, robustness, and efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. To improve matching accuracy, we smooth the noisy raw atomic coordinate data with spline fitting. To improve matching efficiency, we adopt a hierarchical coarse-to-fine strategy. We use an efficient hashing-based technique to screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to discover new, meaningful motifs that were not reported by other structure alignment methods.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"2 ","pages":"169-79"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25833752","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

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

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01

Shu-Yun Le, Jih-H Chen, Jacob V Maize

{"title":"Statistical inference for well-ordered structures in nucleotide sequences.","authors":"Shu-Yun Le, Jih-H Chen, Jacob V Maize","doi":"","DOIUrl":"","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 non-coding 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(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 E(diff) 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":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"2 ","pages":"190-6"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25833754","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

Prediction of protein function using protein-protein interaction data. 利用蛋白质-蛋白质相互作用数据预测蛋白质功能。

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2003-01-01 DOI: 10.1109/csb.2002.1039342

Minghua Deng, Kui Zhang, Shipra Mehta, Ting Chen, Fengzhu Sun

{"title":"Prediction of protein function using protein-protein interaction data.","authors":"Minghua Deng, Kui Zhang, Shipra Mehta, Ting Chen, Fengzhu Sun","doi":"10.1109/csb.2002.1039342","DOIUrl":"https://doi.org/10.1109/csb.2002.1039342","url":null,"abstract":"Assigning functions to novel proteins is one of the most important problems in the postgenomic era. Several approaches have been applied to this problem, including the analysis of gene expression patterns, phylogenetic profiles, protein fusions, and protein-protein interactions. In this paper, we develop a novel approach that employs the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of protein's interaction partners. For each function of interest and protein, we predict the probability that the protein has such function using Bayesian approaches. Unlike other available approaches for protein annotation in which a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We employ our method to predict protein functions based on \"biochemical function,\" \"subcellular location,\" and \"cellular role\" for yeast proteins defined in the Yeast Proteome Database (YPD, www.incyte.com), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data. The supplementary data is available at www-hto.usc.edu/~msms/ProteinFunction.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"10 6 1","pages":"947-60"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2002.1039342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62214316","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: 47