2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第4页

Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method 通过copula方法建模具有多基因型和表型的肿瘤基因通路网络

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925734

Le Bao, Zhou Zhu, Jingjing Ye

{"title":"Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method","authors":"Le Bao, Zhou Zhu, Jingjing Ye","doi":"10.1109/CIBCB.2009.4925734","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925734","url":null,"abstract":"Identification of interactions between molecular features (e.g. mutation, gene expression change) and gross phenotypes in diseases and other biological processes is one of the important challenges in genomic research. Popular approaches such as GSEA are limited to hypothesis tests of bivariate association. However, a specific phenotype is often dependent upon multiple molecular features. It is thus worth considering all possible interactions jointly for a more precise and realistic representation of the cellular network. In this article, a semiparametric copula model is developed to jointly model genotypes, pathways and phenotypes to accomplish this object. A two-step procedure for reconstruction of the network is described. Simulation studies indicate that the method is effective and accurate for the network reconstruction. Application using NCI60 cancer cell line data identifies several subsets of molecular features that jointly perform as the predictors of clinical phenotypes. The copula model is expected to have a broad impact on biomedical research, ranging from cancer treatment to disease prevention.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115697472","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

SARNA-ensemble-predict: The effect of different dissimilarity metrics on a novel ensemble-based RNA secondary structure prediction algorithm SARNA-ensemble-predict:不同的不相似性指标对一种新的基于ensemble的RNA二级结构预测算法的影响

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925701

Herbert H. Tsang, K. Wiese

{"title":"SARNA-ensemble-predict: The effect of different dissimilarity metrics on a novel ensemble-based RNA secondary structure prediction algorithm","authors":"Herbert H. Tsang, K. Wiese","doi":"10.1109/CIBCB.2009.4925701","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925701","url":null,"abstract":"Recently, there is a resurgence of interest in the RNA secondary structure prediction problem due to the discovery of many new families of non-coding RNAs with a variety of functions. This paper describes and presents a novel algorithm for RNA secondary structure prediction based on an ensemble-based approach. An evaluation of the performance in terms of sensitivity and specificity is made. Experiments were performed on eleven structures from four RNA classes (RNaseP, Group I intron 16S rRNA, Group I intron 23S rRNA and 16S rRNA). Three RNA secondary structure similarity metrics (base pair distance, tree edit distance, and thermodynamic energy distance) and their effects on the clustering algorithm were explored. The significant contribution of this paper is in the examining of the various results from employing different dissimilarity metrics. Overall, the base pair distance dissimilarity metric shows better results with the other two distance metrics (tree edit distance and thermodynamic energy distance). The results presented in this paper demonstrate that SARNA-Ensemble-Predict can give comparable performance to a state-of-the-art algorithm Sfold in terms of sensitivity.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116371081","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

Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble 利用神经网络集合预测革兰氏阴性菌的蛋白质亚细胞位置

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925716

Junwei Ma, Wenqi Liu, Hong Gu

引用次数: 4

Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images 苏木精和伊红染色组织学图像颜色模型-分类器对的统计比较

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925740

Mutlu Mete, U. Topaloglu

引用次数: 15

Using diffusion characters for the taxonomy of self-organizing social networks 利用扩散特征对自组织社会网络进行分类

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925708

D. Ashlock, Colin Lee

引用次数: 2

A system for recognition of biological patterns in toxins using computational intelligence 利用计算智能识别毒素中的生物模式的系统

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925717

B. P. Carvalho, T. M. Mendes, Ricardo de Souza Ribeiro, Ricardo Fortuna, José Marcos Veneroso, M. Mudado

{"title":"A system for recognition of biological patterns in toxins using computational intelligence","authors":"B. P. Carvalho, T. M. Mendes, Ricardo de Souza Ribeiro, Ricardo Fortuna, José Marcos Veneroso, M. Mudado","doi":"10.1109/CIBCB.2009.4925717","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925717","url":null,"abstract":"This work presents an innovative way to find biological patterns in toxins in order to classify them according to their biological functions. Basing on relevant biological information (database) it was developed a system that uses computational intelligence to discover novel patterns within the primary and secondary structures of a set of toxins. The discovered patterns make it possible to differentiate these toxins by their function: binding to specific channels for sodium, calcium or potassium ions. The classification rules are built using a given toxin database which is pre-processed according to the existence of signal peptide or propeptide in the primary sequence, together with the predicted secondary structures and its physical and chemical characteristics and water affinity information. The best obtained patterns are combined together in order to generate a final rule. All the experiments were performed using 802 toxin primary sequences labeled as channel functions obtained from two public databases, ATDB and Tox-Prot. After using the system to solve three different binary classification problems, each one for a specific ion channel, a committee is used to obtain the final classification label for each toxin. The committee got a classification accuracy of 80%, with correctness of 97%, 67% and 55% respectively to sodium, potassium and calcium channels.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127672242","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: 1

Detecting sequence and structure homology via an integrative kernel: A case-study in recognizing enzymes 通过整合核检测序列和结构同源性:一个识别酶的案例研究

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925706

Isye Arieshanti, M. Bodén, S. Maetschke, Fabian A. Buske

引用次数: 0

Diagnostic character location within the cryptic skipper butterfly species complex with an evolutionary algorithm 基于进化算法的隐跳蝶种复合体诊断特征定位

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925713

D. Ashlock, T. V. Königslöw

{"title":"Diagnostic character location within the cryptic skipper butterfly species complex with an evolutionary algorithm","authors":"D. Ashlock, T. V. Königslöw","doi":"10.1109/CIBCB.2009.4925713","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925713","url":null,"abstract":"This study presents an evolutionary algorithm for locating DNA sequence characters that are diagnostic between closely related groups of species. The algorithm is developed using synthetic data and then tested on biological data from a species of butterfly recently discovered to be a cryptic complex of species. This technique proved to be successful in locating positions that are diagnostic of the cryptic neotropical skipper butterfly species within the cytochrome c oxidase subunit I (COI) DNA barcode data. The algorithm uses a novel subset representation to select positions within the DNA sequences. A crossover operator that takes pairs of subsets to pairs of subsets is designed. This crossover operator permits the use of a novel mutation operator that disrupts loci showing evidence of convergence, yielding better preservation of diversity in the evolving population of diagnostic character positions. A lexical (tie breaking) fitness function is used to smooth the fitness landscape. The problem of locating diagnostic positions in DNA sequences proved difficult without lexical fitness; with that innovation in place the problem is quite tractable. The evolutionary algorithm developed has the potential for broad application such as in conservation, customs enforcement, and forensics.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130350285","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

Protein fold recognition with adaptive local hyperplane algorithm 基于自适应局部超平面算法的蛋白质折叠识别

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925710

V. Kecman, Tao Yang

引用次数: 19

A multi-threaded DNA tag/anti-tag library generator for multi-core platforms 一个多线程DNA标签/反标签库生成器，用于多核平台

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925724

A. Ravindran, Daniel J. Burns

引用次数: 0