2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献

Classifying Cytochrome c Oxidase subunit 1 by translation initiation mechanism using side effect machines 利用副作用机对细胞色素c氧化酶亚基1的翻译起始机制进行分类

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510703

J. Schonfeld, D. Ashlock

引用次数: 8

A comparative study of the time-series data for inference of gene regulatory networks using B-Spline 用b样条推断基因调控网络的时间序列数据比较研究

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510596

Haixin Wang, James E. Glover, Lijun Qian

引用次数: 4

DGA: Decomposition with genetic algorithm for multiple sequence alignment 基于遗传算法的多序列比对分解

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510595

F. Naznin, R. Sarker, D. Essam

引用次数: 2

Towards a temporal modeling of the genetic network controlling Systemic Acquired Resistance in Arabidopsis thaliana 拟南芥系统获得性抗性遗传网络的时间模型研究

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510589

A. Tchagang, Heather L. Shearer, Sieu Phan, Hugo Bérubé, Fazel Famili, P. Fobert, Youlian Pan

引用次数: 2

Using decision trees to study the convergence of phylogenetic analyses 用决策树研究系统发育分析的收敛性

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510326

Grant R. Brammer, T. Williams

引用次数: 3

Paroxysmal Atrial Fibrillation diagnosis based on feature extraction and classification 基于特征提取与分类的阵发性心房颤动诊断

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510702

B. Pourbabaee, C. Lucas

引用次数: 5

Identification of a gene expression core signature for Duchenne muscular dystrophy (DMD) via integrative analysis reveals novel potential compounds for treatment 通过综合分析鉴定杜氏肌营养不良症(DMD)的基因表达核心特征，揭示了新的潜在治疗化合物

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510485

Noru Ichim-Moreno, M. Aranda, C. Voolstra

{"title":"Identification of a gene expression core signature for Duchenne muscular dystrophy (DMD) via integrative analysis reveals novel potential compounds for treatment","authors":"Noru Ichim-Moreno, M. Aranda, C. Voolstra","doi":"10.1109/CIBCB.2010.5510485","DOIUrl":"https://doi.org/10.1109/CIBCB.2010.5510485","url":null,"abstract":"Duchenne muscular dystrophy (DMD) is a recessive X-linked form of muscular dystrophy and one of the most prevalent genetic disorders of childhood. DMD is characterized by rapid progression of muscle degeneration, and ultimately death. Currently, glucocorticoids are the only available treatment for DMD, but they have been shown to result in serious side effects. The purpose of this research was to define a core signature of gene expression related to DMD via integrative analysis of mouse and human datasets. This core signature was subsequently used to screen for novel potential compounds that antagonistically affect the expression of signature genes. With this approach we were able to identify compounds that are 1) already used to treat DMD, 2) currently under investigation for treatment, and 3) so far unknown but promising candidates. Our study highlights the potential of meta-analyses through the combination of datasets to unravel previously unrecognized associations and reveal new relationships.","PeriodicalId":340637,"journal":{"name":"2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"577 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115824424","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

Integrating multiple scoring functions to improve protein loop structure conformation space sampling 集成多重评分函数，提高蛋白质环结构构象空间采样

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510687

Yaohang Li, I. Rata, E. Jakobsson

引用次数: 6

Neural grammar networks for toxicology 毒理学的神经语法网络

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510322

Christopher J. F. Cameron, Eddie Y. T. Ma, Timothy C. Kremer

{"title":"Neural grammar networks for toxicology","authors":"Christopher J. F. Cameron, Eddie Y. T. Ma, Timothy C. Kremer","doi":"10.1109/CIBCB.2010.5510322","DOIUrl":"https://doi.org/10.1109/CIBCB.2010.5510322","url":null,"abstract":"In this paper we compare two methods for toxicity prediction: a novel method called a neural grammar network (NGN) and a more conventional Quantitative Structure Activity Relation (QSAR) approach based on a feed forward artificial neural network (ANN). Focusing each round of training and prediction on target organisms and specific organ systems sufficiently narrows down the parameters for us to do useful toxicity prediction. We represent the molecules in the dataset two ways. Simplified Molecular Input Line Entry Specification (SMILES) are input to the NGN while Feature vectors (or chemical descriptors) are input to the ANN. We perform training and testing on a regression-type problem wherein we predict the Lethal Dose for 50% (LD50) of the population of a given organism for the molecules in each dataset. The results of the experiment indicates that the SMILES-NGN method outperformed the ANN method in QSAR. The SMILES-NGN estimates were closer to their targets for 87% of the trials on randomized training data (as described in Section II.B) and 62% on grouped data when compared to ANN. The results also showed less variance in 87% of cases for NGN-SMILES estimates compared to ANN. Using a toxicity prediction method such as the one presented here allows the prediction of toxicity without the need for costly lab experiment (and which are, by definition, lethal to the test subjects).","PeriodicalId":340637,"journal":{"name":"2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116708041","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

An exploration of individual RNA structural elements in RNA gene finding RNA基因发现中单个RNA结构元件的探索

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2010-05-02 DOI: 10.1109/CIBCB.2010.5510328

N. Erho, K. Wiese

引用次数: 5