检索结果-内蒙古大学图书馆

ieee International symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Srinivas Aluru David Bader Paul Medvedev

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

ISBN: (纸本)9781538655566;9781538655559

关键词： Conferences bioinformatics Distributed processing computational biology Distributed databases Complexity theory Genomics

来源：评论

学校读者我要写书评

暂无评论

Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: beyond edge weights in psychological networks

Structural and parametric uncertainties in full Bayesian and...

引用

ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Hullam, Gabor Juhasz, Gabriella Deakin, Bill Antal, Peter Budapest Univ Technol & Econ Dept Measurement & Informat Syst Budapest Hungary Hungarian Acad Sci MTA SE Neuropsychopharmacol & Neurochem Res Grp Budapest Hungary Semmelweis Univ Dept Pharmacodynam Fac Pharm Budapest Hungary Univ Manchester Fac Med & Human Sci Sch Community Based Med Neurosci & Psychiat Unit Manchester Lancs England

ISBN: (纸本)9781467389884

Uncertainty over model structures poses a challenge for many approaches exploring effect strength parameters at system-level. Monte Carlo methods for full Bayesian model averaging over model structures require considerable computational resources, whereas bootstrapped graphical lasso and its approximations offer scalable alternatives with lower complexity. Although the computational efficiency of graphical lasso based approaches has prompted growing number of applications, the restrictive assumptions of this approach are frequently ignored. We demonstrate using an artificial and a real-world example that full Bayesian averaging using Bayesian networks provides detailed estimates through posterior distributions for structural and parametric uncertainties and it is a feasible alternative, which is routinely applicable in mid-sized biomedical problems with hundreds of variables. We compare Bayesian estimates with corresponding frequentist quantities from bootstrapped graphical lasso using pairwise Markov Random Fields, discussing also their different interpretations. We present results using synthetic data from an artificial model and using the UK Biobank data set to construct a psychopathological network centered around depression (this research has been conducted using the UK Biobank Resource under Application Number 1602).

关键词： Bayes methods computational modeling Data models Analytical models Markov random fields Uncertainty

来源：评论

学校读者我要写书评

暂无评论

Guest Editors Introduction to the Special Section on ISBRA 2014

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第2期14卷 314-315页

作者： Basu, Mitra Pan, Yi Wang, Jianxin Natl Sci Fdn Comp & Informat Sci & Engn Directorate 4201 Wilson Blvd Arlington VA 22230 USA Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA Cent S Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China

The papers in this special section were presented at the 10th International symposium on bioinformatics Research and Applications ISBRA 2014, which was held at Zhangjiajie, China, June 28-30, 2014.

关键词： Special issues and sections Meetings bioinformatics Biomedical engineering

来源：评论

学校读者我要写书评

暂无评论

Matching Multiple Rigid Domain Decompositions of Proteins

IEEE TRANSACTIONS ON NANOBIOSCIENCE

引用

ieee TRANSACTIONS ON NANOBIOSCIENCE 2017年第2期16卷 81-90页

作者： Flynn, Emily Streinu, Ileana Stanford Univ Biomed Informat Program Stanford CA 94305 USA Smith Coll Dept Comp Sci Northampton MA 01063 USA Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA

We describe efficient methods for consistently coloring and visualizing collections of rigid cluster decompositions obtained from variations of a protein structure, and lay the foundation for more complex setups, that may involve different computational and experimental methods. The focus here is on three biological applications: the conceptually simpler problems of visualizing results of dilution and mutation analyses, and the more complex task of matching decompositions of multiple Nucleic Magnetic Resonance (NMR) models of the same protein. Implemented into the KINematics And RIgidity (KINARI) web server application, the improved visualization techniques give useful information about protein folding cores, help examining the effect of mutations on protein flexibility and function, and provide insights into the structural motions of Protein Data Bank proteins solved with solution NMR. These tools have been developed with the goal of improving and validating rigidity analysis as a credible coarse-grained model capturing essential information about a protein's slow motions near the native state.

关键词： Proteins computational biology/computational biochemistry computational geometry bipartite graphs/optimal matching nanobiotechnology nuclear magnetic resonance (NMR)

来源：评论

学校读者我要写书评

暂无评论

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

引用

ieee/ACM Transactions on computational biology and bioinformatics 2020年第5期18卷

作者： Muhammad Aizaz Akmal Waqar Hussain Nouman Rasool Yaser Daanial Khan Sher Afzal Khan Kuo-Chen Chou Department of Computer Science University of Management and Technology Lahore Punjab Pakistan National Center of Artificial Intelligence Punjab University College of Information Technology University of the Punjab Lahore Punjab Pakistan Dr Panjwani Center for Molecular Medicine and Drug Research International Center for Chemical and Biological Sciences University of Karachi Karachi Sindh Pakistan Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Jeddah KSA Department of Computer Sciences Abdul Wali Khan University Mardan Khyber Pakhtunkhwa Pakistan

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

关键词：

来源：评论

学校读者我要写书评

暂无评论

How to Select a Winner in Evolutionary Optimization?

How to Select a Winner in Evolutionary Optimization?

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Miikkulainen, Risto Shahrzad, Hormoz Duffy, Nigel Long, Phil Univ Texas Austin Sentient Technol Austin TX 78712 USA Sentient Technol San Francisco CA USA EY London England Google Menlo Pk CA USA

ISBN: (纸本)9781538627266

In many evolutionary optimization domains evaluations are noisy. The candidates are tested on a number of randomly drawn samples, such as different games played, different physical simulations, or different user interactions. As a result, selecting the winner is a multiple hypothesis problem: The candidate that evaluated the best most likely received a lucky selection of samples, and will not perform as well in the future. This paper proposes a technique for selecting the winner and estimating its true performance based on the smoothness assumption: Candidates that are similar perform similarly. Estimated fitness is replaced by the average fitness of candidate's neighbors, making the selection and estimation more reliable. Simulated experiments in the multiplexer domain show that this technique is reliable, making it likely that the true winner is selected and its future performance is accurately estimated.

关键词： Multiplexing Reliability Estimation Optimization Evolutionary computation Genomics bioinformatics

来源：评论

学校读者我要写书评

暂无评论

CIBCB 2016 - Annual ieee International Conference on computational intelligence in bioinformatics and computational biology

CIBCB 2016 - Annual IEEE International Conference on Computa...

引用

13th ieee International Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2016

ISBN: (纸本)9781467394727

The proceedings contain 40 papers. The topics discussed include: an effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy;control strategies for intelligent adjustment of pressure in intermittent pneumatic compression systems;protein fold identification using machine learning methods on contact maps;compressed sensing denoising for segmentation of localization microscopy data;computational prediction of bacterial type iv-b effectors using c-terminal signals and machine learning algorithms;a cross-entropy method for change-point detection in four-letter DNA sequences;gene selection using interaction information for microarray-based cancer classification;and computational intelligence for metabolic pathway design: application to the pentose phosphate pathway.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Editorial: Special Section on Bio-Inspired Swarm Computing and Engineering

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第1期14卷 1-3页

作者： Tan, Ying Shi, Yuhui Peking Univ Sch Elect Engn & Comp Sci Dept Intelligence Sci Beijing 100873 Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Presents the introductory editorial for this issue of the publication.

关键词： Special issues and sections Particle swarm optimization Ant colony optimization Clustering methods Feature extraction Path planning Biological systems

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial for Special Section on the 11th International Conference on Intelligent Computing (ICIC)

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第5期14卷 1104-1105页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Machine Learning & Syst Biol Lab Shanghai Peoples R China Polytech Bari Elect & Informat Engn Dept I-70126 Bari Italy Indian Inst Technol Dept Biotechnol Madras 600036 Tamil Nadu India

The papers in this special section were presented at the 11th International Conference on Intelligent Computing ICIC held in Fuzhou, China, on August 20-23, 2015. This conference was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Artificial intelligence Machine learning bioinformatics Biomedical engineering Predictive models Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Governing the swarm Controlling a bio-hybrid society of bees & robots with computational feedback loops

Governing the swarm Controlling a bio-hybrid society of bees...

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Stefanec, Martin Szopek, Martina Schmickl, Thomas Mills, Rob Karl Franzens Univ Graz Inst Zool Artificial Life Lab Graz Austria Univ Lisbon BioISI Biosyst & Integrat Sci Inst Lisbon Portugal

ISBN: (纸本)9781538627266

Bio-hybrid systems in which living organisms interact and self-organize with multi-robot systems are a novel approach in engineering and biology. We show here how a group of honeybees and robots can interact in collective decision making and how computer code that adds feedback loops to the system may affect the global system properties. This study contains a series of experiments with living honeybees and robots as well as a cellular-automaton inspired model that is simple, yet still in good agreement with the empirical findings presented here. Using this model, we explore the most likely candidates for local parameters in the proximate mechanisms of the animals, thus further the understanding of this natural system in a context that is relevant for such bio-hybrid manifestations. We identify positive feedback based on bee-to-bee collision and temperature as an important factor governing collective decision making and found the stopping probability after close-encounters among bees as a crucial local parameter. This study is the first step towards using computer and robotic technology to monitor and control complex animal societies like honeybees.

关键词： honeybees swarm robotics swarm intelligence bio-hybrid systems complex systems nonlinear behavior cellular models feedbacks collective decision making

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：