检索结果-内蒙古大学图书馆

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Mundra, Piyushkumar A. Rajapakse, Jagath C. BioInformatics Research Center School of Computer Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781424467662

Correlation is a very widely used filter criterion for gene selection in cancer classification. However, it uses all the training samples in ranking, which may not be equally important for the classification. Using support vectors, we demonstrate that classical correlation coefficient based gene selection is biased because of the sample points away from classification margin. To remove such bias, we use only the support vectors for computation of correlation coefficient and propose a backward elimination based SVcc-RFE algorithm. The proposed method is tested on several benchmark cancer gene expression datasets and the results show improvement in classification performance compared to other state-of-the-art methods. © ieee.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Integrating multiple scoring functions to improve protein loop structure conformation space sampling

Integrating multiple scoring functions to improve protein lo...

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Li, Yaohang Rata, Ionel Jakobsson, Eric Department of Computer Science North Carolina A and T State University Greensboro NC 27411 United States Center for Biophysics and Computational Biology University of Illinois at Urbana-Champaign Urbana-Champaign IL 61801 United States Department of Molecular and Integrative Physiology National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana-Champaign IL 61801 United States

ISBN: (纸本)9781424467662

In this article, we present a new protein structure modeling approach based on multi-scoring functions sampling. The rationale is to integrate multiple carefully-selected physics- or knowledge-based scoring functions to tolerate insensitivity and inaccuracy existing in an individual scoring function so as to improve protein structure modeling accuracy. We apply the multi-scoring function sampling approach to protein loop backbone structure modeling. Our computational results show that sampling the scoring function space of a physics-based soft-sphere potential function and a knowledge-based scoring function based on pairwise atoms distance has led to resolution improvement in the predicted decoy populations in a set of 12-residue benchmark loop targets. © ieee.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Super-resolution of mammograms

Super-resolution of mammograms

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Zheng, Jun Fuentes, Olac Leung, Ming-Ying Department of Computer Science University of Texas at El Paso El Paso TX United States Department of Mathematical Sciences Bioinformatics Program University of Texas at El Paso El Paso TX United States

ISBN: (纸本)9781424467662

High-quality mammography is the most effective technology presently available for breast cancer screening. High resolution mammograms usually lead to more accurate diagnoses;however, they require large doses of radiation, which may have harmful effects. In this paper, we present a method to synthesize high-resolution mammograms from low-resolution inputs, which offers the potential of allowing accurate diagnoses while minimizing risks to patients. Our algorithm combines statistical machine learning methods and stochastic search to learn the mapping from low-resolution to high-resolution mammograms using a large dataset of training image pairs. Experimental results show that the super-resolution algorithm can generate high-quality, high-resolution breast mammograms from low-resolution input with no human intervention. © ieee.

关键词： Optical resolving power

来源：评论

学校读者我要写书评

暂无评论

Computation intelligence method to find generic non-coding RNA search models

Computation intelligence method to find generic non-coding R...

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Smith, Jennifer A. State University Dept. of Electrical and Computer Engineering MS-2075 1910 University Ave. Boise ID 83725-2075 United States

ISBN: (纸本)9781424467662

Fairly effective methods exist for finding new noncoding RNA genes using search models based on known families of ncRNA genes (for example covariance models). However, these models only find new members of the existing families and are not useful in finding potential members of novel ncRNA families. Other problems with family-specific search include large processing requirements, ambiguity in defining which sequences form a family and lack of sufficient numbers of known sequences to properly estimate model parameters. An ncRNA search model is proposed which includes a collection of non-overlapping RNA hairpin structure covariance models. The hairpin models are chosen from a hairpin-model list compiled from many families in the Rfam non-coding RNA families database. The specific hairpin models included and the overall score threshold for the search model is determined through the use of a genetic algorithm. © ieee.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Modeling treatment and drug effects at the molecular level using hybrid system theory

Modeling treatment and drug effects at the molecular level u...

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Li, Xiangfang Qian, Lijun Dougherty, Edward R. Department of Electrical and Computer Engineering Texas A and M University College Station TX 77843 United States Department of Electrical and Computer Engineering Prairie View A and M University Prairie View TX 77446 United States Phoenix AZ 85004 United States Department of Bioinformatics and Computational Biology Texas M. D. Anderson Cancer Center Houston TX 77030 United States

ISBN: (纸本)9781424467662

In this paper, we propose to study the treatment and drug effects at the molecular level using a hybrid system model. Specifically, we propose a generic piecewise linear model to analyze drug effects on the state of the genes in a genetic regulatory network. We intend to answer the following question: given an initial state, would a treatment or drug (control input) drive the target gene to a new desired state that are not reachable without the treatment or drug? assuming that the concentration level of the drug remains constant. In other words, we try to identify whether there is a chance that the treatment or drug will be effective for changing gene expressions at all. We provide detailed analysis for two cases. In the first case, there is only one target gene;while in the second case, there is also another gene interacting with the target gene. The relationships between various parameters (of the genetic regulatory network and the design of the drug) and the convergence and the steady state of the controlled genes are derived analytically and discussed in detail. Simulations are performed using MATLAB/SIMULINK and the results confirmed our analytical findings. © ieee.

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

Data Mining in Cancer Research

引用

ieee computational intelligence MAGAZINE 2010年第1期5卷 14-18页

作者： Lisboa, Paulo J. G. Vellido, Alfredo Tagliaferri, Roberto Napolitano, Francesco Ceccarelli, Michele Martin-Guerrero, Jose D. Biganzoli, Elia Liverpool John Moores Univ Liverpool L3 5UX Merseyside England Tech Univ Catalonia Girona Spain Univ Salerno Salerno Italy Univ Sannio Benevento Italy Univ Valencia E-46003 Valencia Spain Univ Milan I-20122 Milan Italy

This article is not intended as a comprehensive survey of data mining applications in cancer. Rather, it provides starting points for further, more targeted, literature searches, by embarking on a guided tour of computational intelligence applications in cancer medicine, structured in increasing order of the physical scales of biological processes.

关键词： Data mining Cancer Biomedical monitoring Diseases Clustering algorithms Stability Systems biology Principal component analysis Cells (biology) bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Side effect machines for quaternary edit metric decoding

Side effect machines for quaternary edit metric decoding

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Brown, Joseph Alexander Houghten, Sheridan K. Ashlock, Daniel School of Computer Science University of Guelph Guelph ON N1G 2W1 Canada Department of Computer Science Brock University St. Catharines ON L2S 3A1 Canada Department of Mathematics and Statistics University of Guelph Guelph ON N1G 2W1 Canada

ISBN: (纸本)9781424467662

DNA edit metric codes are used as labels to track the origin of sequence data. This study is the first to treat sophisticated decoders for these error-correcting codes. Side effect machines can provide efficient decoding algorithms for such codes. Two methods for automatically producing decoding algorithms are presented. Side Effect Machines (SEMs), generalizations of finite state automata, are used in both. Single Classifier Machines (SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines (LSEMs) use multiple side effect machines to create a tree structured iterated classification. This study examines these techniques and provides new decoders for existing codes. Presented are ideas for best practises for the creation of these two types of new edit metric decoders. Codes of the form (n,M, d)4 are used in testing due to their suitability for bioinformatics problems. A group of (12, 54-56, 7)4 codes are used as an example of the process. © ieee.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Insect navigation and communication in flight and migration: A potential model for joining and collision avoidance in MAVs and mobile robots fleet control

Insect navigation and communication in flight and migration:...

引用

ieee Conference on Aerospace

作者： Zhanshan Ma Axel W. Krings Richard Millar Feng Wang Jun Chao Computational Biology & Bioinformatics Lab KIZ Chinese Academy of Sciences PRC & Computer Science Department & iBE University of Idaho USA Computer Science Department University of Idaho Moscow ID USA Department of Systems Engineering Naval Postgraduate School MD USA Computer Science Department Kunming University of Science and Technology Kunming China Kunming Institute of Zoology KIZ Chinese Academy and Sciences Kunming China

The human being should be awarded the championship of navigation on the planet, on the virtues that they not only have invented and manufactured the compass, GPS, aircraft, vessel, spacecraft, but also have demonstrated exceptional non-instrumental navigation skills. The Austronesian expansion from the Asian mainland into the Pacific performed by the Pacific navigators, who eventually populated the most remote islands of the Pacific about 4000 years ago, is a vivid example (Wehner 1998). However, animals, especially flying birds and insects are strong contenders. Monarch butterflies can migrate up to 2000 miles from their reproductive sites in the eastern US and Canada to their over-wintering sites in the forests of Mexico, and it is postulated that they may possess a biological equivalent of a low-resolution GPS system that is based on the magnetic field of the earth. In fact, even the long-legged ants (Cataglyphis fortis) in the Saharan desert use the dead-reckoning navigation strategy, which is attributed to the Polynesians, but the ants apparently have acquired the capability much earlier, given the relative short evolutionary history of humans. In this article, we briefly review the state-of-the-art research on insect navigation and communication used in flight and communication, with the objective to inspire cross-disciplinary studies in aerospace engineering, biology and computer science. After a brief review, we overview and identify seven cross-disciplinary research topics that may draw on inspirations from insect navigation and communication in flight and migration. These topics include: ants colony inspired swarm intelligence, honeybee inspired group decision-making, insect sociobiology, MAV/mobile robot flight control and remote control of insect flights, optimal migration strategy, Quorum sensing, and joining and collision avoidance for MAV fleet control. An interesting question one may pose is: given the rich and advanced navigation and communication t

关键词： Insects Navigation Visualization Birds Sun

来源：评论

学校读者我要写书评

暂无评论

Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach

Searching for a single mathematical function to address the ...

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Barton, Alan J. National Research Council Canada's Institute for Information Technology Ottawa ON Canada

ISBN: (纸本)9781424467662

Proteomics involves collecting and analyzing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem;including very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit. This paper extends previous work and explores finding an explicit global function for which two stages are involved: i) computation of a set of candidate functions (results) by the algorithm, and ii) searching within the set for patterns of interest. For the first stage, three classes of approximating global functions are considered: Class 1 functions that have a completely unknown structure, Class 2 functions that have a tiny amount of domain knowledge incorporated, and Class 3 functions that have a small amount of domain knowledge incorporated. For the second stage, some issues with current similarity measures for mathematical expressions are discussed and a new measure is proposed. Preliminary experimental results with an Evolutionary Computation algorithm called Gene Expression Programming (a variant of Genetic Programming) when used with a fuzzy membership within the fitness function are reported. © ieee.

关键词： Peptides

来源：评论

学校读者我要写书评

暂无评论

Classification of HIV-1 protease crystal structures using random forest, linear discriminant analysis and logistic regression

Classification of HIV-1 protease crystal structures using ra...

引用

2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Ko, Gene M. Reddy, A. Srinivas Kumar, Sunil Bailey, Barbara A. Garg, Rajni Computational Science Research Center San Diego State University San Diego CA 92182-1245 United States Department of Electrical and Computer Engineering San Diego State University San Diego CA 92182-1309 United States Department of Mathematics and Statistics San Diego State University San Diego CA 92182-7720 United States

ISBN: (纸本)9781424467662

The present study develops a classification model to correlate the binding pockets of 70 HIV-1 protease crystal structures in terms of their structural descriptors to their complexed HIV-1 protease inhibitors. The Random Forest classification model is used to reduce the chemical descriptor space from 456 to the 12 most relevant descriptors based on the Gini importance measure. The selected 12 descriptors are then used to develop classification models using linear discriminant analysis (LDA) and logistic regression (LR). The top eight descriptors were found to produce the best LDA model with an overall error of 30% and a leave-one-out cross validation error of 44.29%, while the top five descriptors were found to produce the best LR model with an overall error of 28.57% and a leave-one-out cross validation error of 41.43%. Hierarchical clustering was performed on the top five and eight descriptors to verify whether the descriptor selection of Random Forest can group together the binding pockets based on their complexed ligands. The selected descriptors would play a crucial role in understanding the HIV-1 protease binding pocket structure in terms of its chemical descriptors. © ieee.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：