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ieee symposium on computational intelligence in bioinformatics and computational biology

作者： U. Srinivasulu Reddy Michael Arock A. V. Reddy Department of Computer Applications National Institute of Technology

ISBN: (纸本)9781467358743

In bioinformatics, Planted (l, d)-Motif finding is an important and challenging problem, which has many applications. Generally, it is to locate recurring patterns in the promoter regions of co-expressed or co-regulated genes. As we can't expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NP-complete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. These solutions are either "exact" or "approximate". All the proposed exact solutions take exponential-time;they need more time to search for larger parameters l and d. The problems of bioinformatics seldom need the exact optimum solution;rather what they need is robust, fast and near optimal solutions. Therefore, it is impractical to use an exact algorithm to search for large parameters of motifs in real biological dataset. In this paper, we have adopted the features of the Particle Swarm Optimization (PSO) with k-nearest neighbor algorithm to solve the Planted (l, d)-Motif Finding Problem. PSO is a global approximation optimization technique and has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have performed some experiments on synthetic data by increasing number of sequences and the length of the sequences for different (l, d)-Motifs for the following data sets: general instances (10, 2), (11, 2), (12, 3), (15, 4), (16, 5), (18, 6), (20, 7) (30, 11) and (40,15). Challenging instances: (9, 2), (11, 3), (13, 4), (15, 5), (20, 7), (30, 11), (40, 15) and finally, we have applied our proposed method for real biological sequences. From the experimental results we observe that the proposed algorithm is more efficient and accurate compared to existing approximation algorithms and even it works better for larger motif instances.

关键词： Motif Finding Particle Swarm Optimization (PSO) Swarm intelligence (SI) Transcriptional Factor Binding Sites (TFBS) Gene regulation Planted (l D)-Motifs K-Nearest-Neighbor Algorithm (KNN)

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2007 ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB 2007) [Conference Reports]

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ieee computational intelligence Magazine 2008年第1期3卷 78-79页

作者： L. Gwenn Volkert

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Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第2期10卷 286-299页

作者： Maji, Pradipta Paul, Sushmita Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

Gene expression data clustering is one of the important tasks of functional genomics as it provides a powerful tool for studying functional relationships of genes in a biological process. Identifying coexpressed groups of genes represents the basic challenge in gene clustering problem. In this regard, a gene clustering algorithm, termed as robust rough-fuzzy c-means, is proposed judiciously integrating the merits of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition, the integration of probabilistic and possibilistic memberships of fuzzy sets enables efficient handling of overlapping partitions in noisy environment. The concept of possibilistic lower bound and probabilistic boundary of a cluster, introduced in robust rough-fuzzy c-means, enables efficient selection of gene clusters. An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on 14 yeast microarray data sets.

关键词： Microarray gene clustering overlapping clustering rough sets fuzzy sets

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Integrative functional statistics in logic programming

Integrative functional statistics in logic programming

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15th International symposium on Practical Aspects of Declarative Languages, PADL 2013

作者： Angelopoulos, Nicos Santos Costa, Vítor Azevedo, João Wielemaker, Jan Camacho, Rui Wessels, Lodewyk Bioinformatics and Statistics Netherlands Cancer Institute Amsterdam Netherlands Netherlands CRACS-INESC Porto LA Universidade Do Porto 4169-007 Porto Portugal DCC-FCUP Universidade Do Porto Rua do Campo Alegre 1021/1055 4169-007 Porto Portugal LIAAD Faculdade de Engenharia Universidade Do Porto Portugal Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9783642452833

We present r..eal , a library that integrates the R statistical environment with Prolog. Due to R's functional programming affinity the interface introduced has a minimalistic feel. Programs utilising the library syntax are elegant and succinct with intuitive semantics and clear integration. In effect, the library enhances logic programming with the ability to tap into the vast wealth of statistical and probabilistic reasoning available in R. The software is a useful addition to the efforts towards the integration of statistical reasoning and knowledge representation within an AI context. Furthermore it can be used to open up new application areas for logic programming and AI techniques such as bioinformatics, computational biology, text mining, psychology and neuro sciences, where R has particularly strong presence. © 2013 Springer-Verlag.

关键词： Logic programming

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2012 ieee symposium on computational intelligence and computational biology, CIBCB 2012

2012 IEEE Symposium on Computational Intelligence and Comput...

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2012 ieee symposium on computational intelligence and computational biology, CIBCB 2012

ISBN: (纸本)9781467311892

The proceedings contain 58 papers. The topics discussed include: protein model assessment using extended fuzzy decision tree with spatial neighborhood features;quantitative analysis of redundancy in evolution of developmental systems;developing a novel integrated model of p38 MAPK and glucocorticoid signaling pathways;idock: a multithreaded virtual screening tool for flexible ligand docking;multiple worlds model for motif discovery;data mining techniques for AFM- based tumor classification;robust integrated framework for effective feature selection and sample classification and its application to gene expression data analysis;towards predictive structure-based models of evolved drug resistance;learning to predict health status of geriatric patients from observational data;multicenter study desing in survival analysis;and hybrid feature selection method for biomedical datasets.

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An interactive approach to story characterization

An interactive approach to story characterization

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ieee Workshop on Affective computational intelligence (WACI)

作者： Malik Nairat Palle Dahlstedt Mats G. Nordahl Department of Applied Information Technology University of Gothenburg Sweden

We propose an interactive framework where artists can design simple story characters using an agent-based interactive system. The characters' personalities are designed by modifying the dynamics of their internal states while observing their interactions in a virtual environment. This can provide the artist with interesting characters that can be used for further story generation. We also argue that cartoon characters are particularly suited to this kind of generative approach.

关键词： Buildings Silicon Genomics bioinformatics

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How to multiply dynamic programming algorithms

How to multiply dynamic programming algorithms

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8th Brazilian symposium on bioinformatics, BSB 2013

作者： Höner Zu Siederdissen, Christian Hofacker, Ivo L. Stadler, Peter F. Dept. Theoretical Chemistry Univ. Vienna Währingerstr. 17 Wien Austria Bioinformatics and Computational Biology Research Group University of Vienna 1090 Währingerstraße 17 Vienna Austria RTH Univ. Copenhagen Grønnegårdsvej 3 Frederiksberg C Denmark Dept. Computer Science Interdisciplinary Center for Bioinformatics Univ. Leipzig Härtelstr. 16-18 Leipzig Germany MPI Mathematics in the Sciences Inselstr. 22 Leipzig Germany FHI Cell Therapy and Immunology Perlickstr. 1 Leipzig Germany Santa Fe Institute 1399 Hyde Park Rd. Santa Fe United States

ISBN: (纸本)9783319026237

We develop a theory of algebraic operations over linear grammars that makes it possible to combine simple "atomic" grammars operating on single sequences into complex, multi-dimensional grammars. We demonstrate the utility of this framework by constructing the search spaces of complex alignment problems on multiple input sequences explicitly as algebraic expressions of very simple 1-dimensional grammars. The compiler accompanying our theory makes it easy to experiment with the combination of multiple grammars and different operations. Composite grammars can be written out in LATEX for documentation and as a guide to implementation of dynamic programming algorithms. An embedding in Haskell as a domain-specific language makes the theory directly accessible to writing and using grammar products without the detour of an external compiler. http://***/Software/gramprod/. © 2013 Springer International Publishing.

关键词： bioinformatics

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[Front matter]

[Front matter]

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ieee symposium on computational intelligence Applications In Smart Grid (CIASG)

The following topics are dealt with: computational intelligence applications; CIASG; optimization; cyber security; demand side management; distributed energy resources; FACTS; power system planning; power markets; power system economics; power system control; plug-in vehicles; renewable energy; smart micro-grids; smart grid education; smart sensing; synchrophasors; wide area monitoring and power system protection.

关键词： Smart grids Educational institutions Awards activities biology Backpropagation Economics computational intelligence

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Biologically inspired near extinct system reconstruction

Biologically inspired near extinct system reconstruction

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Athanasios Bibas George Spanoudakis Christos Bellos Dimitrios I. Fotiadis Dimitrios Koutsouris Department of Otolaryngology Read & Neck Surgery University of Athens Greece

Recovery software system operations from a state of extensive damage without human intervention is a challenging problem as it may need to be based on a different infrastructure from the one that the system was originally designed for and deployed on (i.e., computational and communication devices) and significant reorganization of system functionalities. In this paper, we introduce a bio-inspired approach for reconstructing nearly extinct complex software systems. Our approach is based on encoding a computational DNA (co-DNA) of a system and computational analogues of biological processes to enable the transmission of co-DNA over computational devices and, through it, the transformation of these devices into system cells that can realise chunks of the system functionality, and spread further its reconstruction process.

关键词： DNA Software systems Organisms Protocols Cells (biology) computational modeling

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A distance-based feature-encoding technique for protein sequence classification in bioinformatics

A distance-based feature-encoding technique for protein sequ...

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ieee International Conference on computational intelligence and Cybernetics (CyberneticsCom)

作者： Muhammad Javed Iqbal Ibrahima Faye Abas Md Said Brahim Belhaouari Samir Computer and Information System Department Universiti Teknologi PETRONAS Malaysia Fundamental and Applied Science Departments Universiti Teknologi PETRONAS Malaysia Colleges of Sciences Alfaisal University Riyadh KSA

bioinformatics has been emerging as a new research dimension since the last century by combining computer science and biology techniques for the automatic analysis of biological sequence data. The volume of the biological data gathered under different sequencing projects is increasing exponentially. These sequences contain extremely important information about genes, their structure and function. computational techniques which involve machine learning and pattern recognition are becoming very useful on bioinformatics data like DNA and protein. Protein classification into different groups could be used for knowing the structure or the function of unknown protein sequence. The process of classifying protein amino acid sequences into a family /superfamily is a very complex problem. However, from among other major issues in a protein classification, the critical one is an accurate representation of amino acid sequence during the feature extraction. In this work, we have proposed a distance-based feature-encoding method; the proposed technique has been tested with different classifiers, which have shown better results than the previously available techniques for superfamily classification of protein sequences. The maximum average classification accuracy obtained was 91.2%. The dataset used in the experiments was taken from the well known UniProtKB protein database.

关键词： Protein sequence Feature extraction Amino acids bioinformatics Accuracy Classification algorithms

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