Proceedings. IEEE Computer Society Bioinformatics Conference最新文献

EUROSIM 2016 - The 9th Eurosim Congress on Modelling and Simulation 2016 -第九届欧洲模拟建模与仿真大会

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2016-06-01 DOI: 10.1109/EUROSIM.2016.49

O. Kilkki, K. Zenger

引用次数: 0

2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, Xi'an, China, September 21-24, 2015 2015情感计算与智能交互国际会议，中国西安，2015年9月21-24日

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2015-01-01 DOI: 10.1109/acii36271.2015

A. Brouwer, C. Dijksterhuis, J. V. Erp

引用次数: 1

Computer Security Foundations Symposium - CSF 2011 计算机安全基础研讨会- CSF 2011

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2011-01-01 DOI: 10.1109/CSF.2011.25

S. Kremer, G. Steel, B. Warinschi

引用次数: 0

Proceedings of the 24th Annual IEEE Conference on Computational Complexity, CCC 2009, Paris, France, 15-18 July 2009 第24届IEEE计算复杂性年会论文集，CCC 2009，法国巴黎，2009年7月15-18日

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2009-01-01 DOI: 10.1109/CCC.2009.33

Andrew Drucker

引用次数: 1

Consumer Electronics, 2005. (ISCE 2005). Proceedings of the Ninth International Symposium on 消费电子，2005。(ISCE 2005)。第九届国际研讨会论文集

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/ISCE.2005.1502398

K. Wong, D. Arvind, N. Sharwood-Smith, Andrew Smith

引用次数: 2

Information Technology and Applications, 2005. ICITA 2005. Third International Conference on 资讯科技及应用，2005。ICITA 2005。第三届…国际会议

Proceedings. IEEE Computer Society Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/ICITA.2005.258

K. Wong, D. Arvind

引用次数: 4

A new similarity measure among protein sequences. 一种新的蛋白质序列相似性度量方法。

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

Kuen-Pin Wu, Hsin-Nan Lin, Ting-Yi Sung, Wen-Lian Hsu

引用次数: 0

Two operative concepts for the post-genomic era: the "mémoire vive" of the cell and a molecular algebra. 后基因组时代的两个有效概念:细胞的“msammoire vive”和分子代数。

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

Simone Bentolila

引用次数: 0

3D structural homology detection via unassigned residual dipolar couplings. 通过未分配的剩余偶极耦合进行三维结构同源性检测。

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

Christopher James Langmead, Bruce Randall Donald

{"title":"3D structural homology detection via unassigned residual dipolar couplings.","authors":"Christopher James Langmead, Bruce Randall Donald","doi":"","DOIUrl":"","url":null,"abstract":"Recognition of a protein's fold provides valuable information about its function. While many sequence-based homology prediction methods exist, an important challenge remains: two highly dissimilar sequences can have similar folds-- how can we detect this rapidly, in the context of structural genomics? High-throughput NMR experiments, coupled with novel algorithms for data analysis, can address this challenge. We report an automated procedure for detecting 3D structural homologies from sparse, unassigned protein NMR data. Our method identifies the 3D structural models in a protein structural database whose geometries best fit the unassigned experimental NMR data. It does not use sequence information and is thus not limited by sequence homology. The method can also be used to confirm or refute structural predictions made by other techniques such as protein threading or sequence homology. The algorithm runs in O(pnk(3)) time, where p is the number of proteins in the database, n is the number of residues in the target protein, and k is the resolution of a rotation search. The method requires only uniform (15)N-labelling of the protein and processes unassigned H(N)-(15)N residual dipolar couplings, which can be acquired in a couple of hours. Our experiments on NMR data from 5 different proteins demonstrate that the method identifies closely related protein folds, despite low-sequence homology between the target protein and the computed model.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"2 ","pages":"209-17"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25833756","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

Discovering compact and highly discriminative features or feature combinations of drug activities using support vector machines. 使用支持向量机发现药物活动的紧凑和高度判别特征或特征组合。

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

Hwanjo Yu, Jiong Yang, Wei Wang, Jiawei Han

{"title":"Discovering compact and highly discriminative features or feature combinations of drug activities using support vector machines.","authors":"Hwanjo Yu, Jiong Yang, Wei Wang, Jiawei Han","doi":"","DOIUrl":"","url":null,"abstract":"Nowadays, high throughput experimental techniques make it feasible to examine and collect massive data at the molecular level. These data, typically mapped to a very high dimensional feature space, carry rich information about functionalities of certain chemical or biological entities and can be used to infer valuable knowledge for the purposes of classification and prediction. Typically, a small number of features or feature combinations may play determinant roles in functional discrimination. The identification of such features or feature combinations is of great importance. In this paper, we study the problem of discovering compact and highly discriminative features or feature combinations from a rich feature collection. We employ the support vector machine as the classification means and aim at finding compact feature combinations. Comparing to previous methods on feature selection, which identify features solely based on their individual roles in the classification, our method is able to identify minimal feature combinations that ultimately have determinant roles in a systematic fashion. Experimental study on drug activity data shows that our method can discover descriptors that are not necessarily significant individually but are most significant collectively.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"2 ","pages":"220-8"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25833757","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