Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003最新文献_第7页

An iterative loop matching approach to the prediction of RNA secondary structures with pseudoknots 带假结的RNA二级结构预测的迭代环匹配方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227394

Jianhua Ruan, G. Stormo, Weixiong Zhang

引用次数: 240

Noise-attenuation in artificial genetic networks 人工遗传网络中的噪声衰减

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227428

Y. Morishita, K. Aihara

引用次数: 0

A new approach to gene prediction using the self-organizing map 利用自组织图谱进行基因预测的新方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227365

Shaun Mahony, Terry J. Smith, J. McInerney, A. Golden

引用次数: 1

An evolutionary approach to finding schemas for 3-class protein secondary structure prediction 3类蛋白质二级结构预测模式的进化寻找方法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227384

Hsiang Chi Huang

引用次数: 3

Haplotype pattern mining & classification for detecting disease associated site 用于疾病相关位点检测的单倍型模式挖掘与分类

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227369

T. Kido, Masanori Baba, Hirohito Matsumine, Yoko Higashi, Hirotaka Higuchi, Masaaki Muramatsu

引用次数: 2

Experimental studies of the universal chemical key (UCK) algorithm on the NCI database of chemical compounds 通用化学密钥(UCK)算法在NCI化合物数据库中的实验研究

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227324

R. Grossman, Pavan Kasturi, D. Hamelberg, B. Liu

引用次数: 10

Finding higher order motifs under the levenshtein measure 在levenshstein测度下寻找高阶基元

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227414

E. Adebiyi, Tinuke Dipe

{"title":"Finding higher order motifs under the levenshtein measure","authors":"E. Adebiyi, Tinuke Dipe","doi":"10.1109/CSB.2003.1227414","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227414","url":null,"abstract":"We study the problem of finding higher order motifs under the levenshtein measure, otherwise known as the edit distance. In the problem set-up, we are given N sequences, each of average length n, over a finite alphabet /spl Sigma/ and thresholds D and q, we are to find composite motifs that contain motifs of length P (these motifs occur with almost D differences) in 1 /spl les/ q /spl les/ N distinct sequences. Two interesting but involved algorithms for finding higher order motifs under the edit distance was presented by Marsan and Sagot. Their second algorithm is much more complicated and its complexity is asymptotically not better. Their first algorithm runs in O(M /spl middot/ N/sup 2/n/sup 1+/spl alpha/ /spl middot/p /spl middot/ pow(/spl epsi/)/) where p /spl ges/ 2, /spl alpha/ > 0, pow(/spl epsi/) is a concave function that is less than 1, /spl epsi/= D/P and M is the expected number of all monad motifs. We present an alternative algorithmic approach also for Edit distance based on the concept described. The resulting algorithm is simpler and runs in O(N/sup 2/n/sup 1+p /spl middot/ pow(/spl epsi/)/) expected time.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"5 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":"128375753","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

Algorithms for bounded-error correlation of high dimensional data in microarray experiments 微阵列实验中高维数据的有界误差相关算法

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227412

Mehmet Koyutürk, A. Grama, W. Szpankowski

引用次数: 2

Clustering time-varying gene expression profiles using scale-space signals 利用尺度空间信号聚类时变基因表达谱

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227303

T. Syeda-Mahmood

{"title":"Clustering time-varying gene expression profiles using scale-space signals","authors":"T. Syeda-Mahmood","doi":"10.1109/CSB.2003.1227303","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227303","url":null,"abstract":"The functional state of an organism is determined largely by the pattern of expression of its genes. The analysis of gene expression data from gene chips has primarily revolved around clustering and classification of the data using machine learning techniques based on the intensity of expression alone with the time-varying pattern mostly ignored. In this paper, we present a pattern recognition-based approach to capturing similarity by finding salient changes in the time-varying expression patterns of genes. Such changes can give clues about important events, such as gene regulation by cell-cycle phases, or even signal the onset of a disease. Specifically, we observe that dissimilarity between time series is revealed by the sharp twists and bends produced in a higher-dimensional curve formed from the constituent signals. Scale-space analysis is used to detect the sharp twists and turns and their relative strength with respect to the component signals is estimated to form a shape similarity measure between time profiles. A clustering algorithm is presented to cluster gene profiles using the scale-space distance as a similarity metric. Multidimensional curves formed from time series within clusters are used as cluster prototypes or indexes to the gene expression database, and are used to retrieve the functionally similar genes to a query gene profile. Extensive comparison of clustering using scale-space distance in comparison to traditional Euclidean distance is presented on the yeast genome database.","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":"127813632","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: 7

Degenerate primer design via clustering 通过聚类简化引物设计

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI: 10.1109/CSB.2003.1227306

Xintao Wei, D. Kuhn, G. Narasimhan

{"title":"Degenerate primer design via clustering","authors":"Xintao Wei, D. Kuhn, G. Narasimhan","doi":"10.1109/CSB.2003.1227306","DOIUrl":"https://doi.org/10.1109/CSB.2003.1227306","url":null,"abstract":"This paper describes a new strategy for designing degenerate primers for a given multiple alignment of amino acid sequences. Degenerate primers are useful for amplifying homologous genes. However, when a large collection of sequences is considered, no consensus region may exist in the multiple alignment, making it impossible to design a single pair of primers for the collection. In such cases, manual methods are used to find smaller groups from the input collection so that primers can be designed for individual groups. Our strategy proposes an automatic grouping of the input sequences by using clustering techniques. Conserved regions are then detected for each individual group. Conserved regions are scored using a blocksimilarity score, a novel alignment scoring scheme that is appropriate for this application. Degenerate primers are then designed by reverse translating the conserved amino acid sequences to the corresponding nucleotide sequences. Our program, DePiCt, was written in BioPerl and was tested on the Toll-Interleukin Receptor (TIR) and the nonTIR family of plant resistance genes. Existing programs for degenerate primer design were unable to find primers for these data sets.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"53 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120877611","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: 33