检索结果-内蒙古大学图书馆

Immunological Computation for Protein Function Prediction

FUNDAMENTA INFORMATICAE 2015年第1期139卷 91-114页

作者： Zekri, Meriem Alem, Karima Souici-Meslati, Labiba Badji Mokhtar Annaba Univ Dept Comp Sci LabGED Lab LISCO Lab Annaba Algeria Badji Mokhtar Annaba Univ LBBM Lab Dept Biochem Annaba Algeria Badji Mokhtar Annaba Univ Dept Comp Sci LISCO Lab Annaba Algeria

Immunological computation is one of the largest recent bio-inspired approaches of artificial intelligence. Artificial immune systems (AIS) are inspired by the processes of the biological immune systems like the learning and memory characteristics which are used for solving complex problems. During the last two decades, AIS have been applied in various fields such as optimization, network security and data mining. In this article, we focus on the application of AIS to data mining in bioinformatics, more specifically, the classification task. For this purpose, we suggest three immune models based on clonal selection theory for the identification of G-protein coupled receptors (GPCRs) to predict their function. Our three classifiers are the artificial immune recognition system (AIRS), the clonal selection algorithm (clonalG) and the clonal selection classification algorithm (CSCA). The GPCRs represent one of the largest and most important families of multifunctional proteins and are a significant target for bioactive and drug discovery programs. It is estimated that more than half of the drugs on the market currently target GPCRs. However, although thousands of GPCRs sequences are known, many of them remain orphans, have unknown function. Our experiments show that the three immunological classifiers have provided interesting results, however, AIRS obtained the best ones. Therefore, it is, for us, the most suitable immune model for the GPCRs identification problem.

关键词： Artificial immune system Protein function prediction GPCRs Classification clonal selection algorithms Pseudo amino acid composition

来源：评论

学校读者我要写书评

暂无评论

Conformational Sampling of a Biomolecular Rugged Energy Landscape

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 732-739页

作者： Rydzewski, Jakub Jakubowski, Rafal Nicosia, Giuseppe Nowak, Wieslaw Nicolaus Copernicus Univ Inst Phys Fac Phys Astron & Informat Grudziadzka 5 PL-87100 Torun Poland Univ Catania Dept Math & Comp Sci Viale A Doria 6 I-95125 Catania Italy

The protein structure refinement using conformational sampling is important in hitherto protein studies. In this paper, we examined the protein structure refinement by means of potential energy minimization using immune computing as a method of sampling conformations. The method was tested on the x-ray structure and 30 decoys of the mutant of [Leu] Enkephalin, a paradigmatic example of the biomolecular multiple-minima problem. In order to score the refined conformations, we used a standard potential energy function with the OPLSAA force field. The effectiveness of the search was assessed using a variety of methods. The robustness of sampling was checked by the energy yield function which measures quantitatively the number of the peptide decoys residing in an energetic funnel. Furthermore, the potential energy-dependent Pareto fronts were calculated to elucidate dissimilarities between peptide conformations and the native state as observed by x-ray crystallography. Our results showed that the probed potential energy landscape of [Leu] Enkephalin is self-similar on different metric scales and that the local potential energy minima of the peptide decoys are metastable, thus they can be refined to conformations whose potential energy is decreased by approximately -250 kJ/mol.

关键词： Structure refinement configuration space self-similarity potential energy landscape clonal selection algorithms

来源：评论

学校读者我要写书评

暂无评论

An immune algorithm for protein structure prediction on lattice models

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2007年第1期11卷 101-117页

作者： Cutello, Vincenzo Nicosia, Giuseppe Pavone, Mario Timmis, Jonathan Univ Catania Dept Math & Comp Sci I-95125 Catania Italy Univ York Dept Comp Sci York YO10 5DD N Yorkshire England Univ York Dept Elect York YO10 5DD N Yorkshire England

We present an immune algorithm (IA) inspired by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation to allow effective searching, and an aging mechanism which is a new immune inspired operator that is devised to enforce diversity in the population during evolution. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. The proposed IA was tested on well-known PSP lattice models, the HP model in two-dimensional and three-dimensional square lattices', and the functional model protein, which is a more realistic biological model. Our experimental results demonstrate that the proposed IA is very competitive with the existing state-of-art algorithms for the PSP on lattice models.

关键词： aging operator clonal selection algorithms functional model proteins hypermacromutation operator hypermutation operator immune algorithms (IAs) protein structure prediction problem two-dimensional HP model three-dimensional HP model

来源：评论

学校读者我要写书评

暂无评论

Automatic multiple circle detection based on artificial immune systems

引用

EXPERT SYSTEMS WITH APPLICATIONS 2012年第1期39卷 713-722页

作者： Cuevas, Erik Osuna-Enciso, Valentin Wario, Fernando Zaldivar, Daniel Perez-Cisneros, Marco Univ Guadalajara Dept Ciencias Computac CUCEI Guadalajara 44430 Jal Mexico

Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Artificial immune systems Computer vision Circle detection clonal selection algorithms

来源：评论

学校读者我要写书评

暂无评论

Hybrid models based on biological approaches for speech recognition

引用

ARTIFICIAL INTELLIGENCE REVIEW 2009年第1-4期32卷 45-57页

作者： Neggaz, Nabil Benyettou, Abdelkader Univ Sci & Technol Oran USTO Algeria Dept Comp Sci Lab Signal Image Parole SIMPA Oran Elmnaouer 31036 Algeria

This paper aims to adapt the clonal selection Algorithm (CSA) which is usually used to explain the basic features of artificial immune systems to the learning of Neural Networks, instead of Back Propagation. The CSA was first applied to a real world problem (IRIS database) then compared with an artificial immune network. CSA performance was contrasted with other versions of genetic algorithms such as: Differential Evolution (DE), Multiple Populations Genetic algorithms (MPGA). The tested application in the simulation studies were IRIS (vegetal database) and TIMIT (phonetic database). The results obtained show that DE convergence speeds were faster than the ones of multiple population genetic algorithm and genetic algorithms, therefore DE algorithm seems to be a promising approach to engineering optimization problems. On the other hand, CSA demonstrated good performance at the level of pattern recognition, since the recognition rate was equal to 99.11% for IRIS database and 76.11% for TIMIT. Finally, the MPGA succeeded in generalizing all phonetic classes in a homogeneous way: 60% for the vowels and 63% for the fricatives, 68% for the plosives.

关键词： clonal selection algorithms Differential evolution Multiple populations genetic algorithms Neural networks Phonetic classification

来源：评论

学校读者我要写书评

暂无评论

A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

引用

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2014年第1期2014卷 1页

作者： Shu, Wanneng Wang, Wei Wang, Yunji South Cent Univ Nationalities Coll Comp Sci Wuhan 430074 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Peoples R China Univ Texas San Antonio Elect & Comp Engn Dept San Antonio TX 78249 USA

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

关键词： Green cloud computing Dynamic voltage and frequency scaling Resource allocation Service level agreement clonal selection algorithms

来源：评论

学校读者我要写书评

暂无评论

Design and characterization of effective solar cells

引用

ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2022年第2期13卷 355-382页

作者： Ojha, Varun Jansen, Giorgio Patane, Andrea La Magna, Antonino Romano, Vittorio Nicosia, Giuseppe Univ Reading Dept Comp Sci Reading Berks England Univ Cambridge Cambridge Syst Biol Ctr Dept Biochem Cambridge England Univ Oxford Dept Comp Sci Oxford England CNR Inst Microelect & Microsyst Catania Italy Univ Catania Dept Math & Comp Sci Catania Italy Univ Cambridge Syst Biol Ctr Cambridge England Univ Catania Dept Biomed & Biotechnol Sci Catania Italy

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency (Q(e)) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, Q(e) of a cell with its TCO layer's doping methods and TCO and BR layer's material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

关键词： Thin-film silicon solar cell Quantum efficiency Maxwell simulation Multi-objective optimization Pareto optimality Artificial immune systems Optimization Immunological algorithms OptIA-II clonal selection algorithms

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid clonal selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

引用

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016年第1期2016卷 1-14页

作者： Zhang, Weiwei Lin, Jingjing Jing, Honglei Zhang, Qiuwen Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 450000 Peoples R China

Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.

关键词： clonal selection algorithms IMMUNE system RECOMBINATION (Chemistry) MUTATION (Biology) GENETIC recombination

来源：评论

学校读者我要写书评

暂无评论

An immunological algorithm for global numerical optimization

引用

7th International Conference on Evolution Artificielle

作者： Cutello, Vincenzo Narzisi, Giuseppe Nicosia, Giuseppe Pavone, Mario Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

ISBN: (纸本)3540335897

Numerical optimization of given objective functions is a crucial task in many real-life problems. The present article introduces an immunological algorithm for continuous global optimization problems, called OPT-IA. Several biologically inspired algorithms have been designed during the last few years and have shown to have very good performance on standard test bed for numerical optimization. In this paper we assess and evaluate the performance of OPT-IA, FEP, IFEP, DIRECT, CEP, PSO, and EO with respect to their general applicability as numerical optimization algorithms. The experimental protocol has been performed on a suite of 23 widely used benchmarks problems. The experimental results show that OPT-IA is a suitable numerical optimization technique that, in terms of accuracy, generally outperforms the other algorithms analyzed in this comparative study. The OPT-IA is also shown to be able to solve large-scale problems.

关键词： artificial immune systems clonal selection algorithms immune algorithm aging operator global numerical optimization

来源：评论

学校读者我要写书评

暂无评论

Determination of protein structure and dynamics combining immune algorithms and pattern search methods

引用

Natural Computing 2007年第1期6卷 55-72页

作者： Anile, A.M. Cutello, V. Narzisi, G. Nicosia, G. Spinella, S. Department of Mathematics and Computer Science University of Catania 95125 Catania V.le A. Doria 6 Italy Department of Linguistics University of Calabria 87036 Arcavata di Rende CS Ponte P. Bucci 17/B Italy

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multiple-minima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms. © Springer Science+Business Media, Inc. 2007.

关键词： clonal selection algorithms DIRECT Immune algorithms Pattern search methods Protein folding Protein structure prediction Structural bioinformatics

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：