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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology"

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Non-coding RNA gene finding with combined partial covariance models

Non-coding RNA gene finding with combined partial covariance...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Wenbo Jiang Kay C. Wiese School of Computing Science Simon Fraser University Surrey BC Canada

Covariance models provide excellent accuracy for ncRNA homology search. However, high computational complexity has limited their usefulness. This research improves the covariance model's search efficiency by building combined models for a group of different RNA families, which is selected using a clustering strategy. A series of combined partial covariance models are built from the stem loop structural elements that the ncRNA gene families share. Experimental results suggest that for most RNA gene families investigated, our combination search method successfully provides run time improvement with acceptable accuracy. Although there still exist limitations such as recall loss for a few RNA gene families, this novel combination approach has implications for future studies of reducing covariance model's search complexity.

关键词： RNA Genomics bioinformatics computational modeling Buildings Complexity theory Databases

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Second workshop on Algorithms for Large-Scale Information Processing in knowledge discovery (ALSIP)

Second workshop on Algorithms for Large-Scale Information Pr...

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3rd International symposium on Artificial intelligence, JSAI-isAI 2011

作者： Tsuda, Koji Minato, Shin-Ichi Computational Biology Research Center National Institute of Advanced Industrial Science and Technology Japan Graduate School of Information Science and Technology Hokkaido University Japan JST ERATO Minato Discrete Structure Manipulation System Project Japan

The Second Workshop on Algorithms for Large-Scale Information Processing in Knowledge Discovery (ALSIP 2011) is held on December 1-2 at Takamatsu Sunport Hall, Takamatsu, Japan. This workshop is a part of JSAI Symposi... 详细信息

ISBN: (纸本)9783642320897

关键词： Artificial intelligence

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A model of competitive exclusion in plants

A model of competitive exclusion in plants

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Daniel Ashlock Erin Wild Department of Mathematics and Statistics University of Guelph Guelph ONT Canada

Grid plants are a simple artificial organism that models competitive exclusion in annual plants. Simulated plants that grow only from their tip are placed on a toroidal grid. They grow according to genetic plans that are expressed, limited by both energy and other plants. Once a plant has occupied a cell of the grid, no other may, so that the plants are competing for space. The algorithm simulates reproduction for 1000 model years under differing conditions. The final populations of plants are compared using a variety of tools including agent case embeddings, non-linear projection, and hierarchical clustering. It is found the plants adapt strongly to the differing conditions with higher seed mortality rates corresponding to more aggressive seed production. Performance of the plants is visualized in a number of ways and suggestions are made for generalizing and applying the model.

关键词： Genomics bioinformatics Biological system modeling Vectors Production Games Mathematical model

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Comparison of RSH with shape signatures and MACT technique for shape analysis of HIV-1 protein

Comparison of RSH with shape signatures and MACT technique f...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Alla S. Reddy Himali Desai Sunil Kumar Rajni Garg Electrical and Computer Engineering Department San Diego State University San Diego CA USA Computational Science Research Center San Diego State University San Diego CA USA

A comparative analysis of three important shape analysis techniques viz. real spherical harmonic (RSH) coefficient, shape signatures based on ray-tracing, and multi-resolution attributed contour tree (MACT) is performed to understand their strengths and weaknesses in terms of their shape description power and computational ability. The analysis is performed on anti-HIV ligands extracted from NCI database and the binding pockets of the HIV-1 protease proteins extracted from PDB. The results demonstrate that the RSH technique is more sensitive to capture the detailed shape changes of the ligand molecules and protein binding pockets over the other two techniques.

关键词： Shape Proteins Three dimensional displays Databases Drugs Electrostatics Electric potential

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An ontology design methodology for Knowledge-Based systems with application to bioinformatics

An ontology design methodology for Knowledge-Based systems w...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： A. Fiannaca M. La Rosa Riccardo Rizzo Alfonso Urso Salvatore Gaglio Riccardo Rizzo. Alfonso Urso ICAR-CNR National Research Council Palermo Italy DICGIM University of Palermo Palermo Italy

Ontologies are formal knowledge representation models. Knowledge organization is a fundamental requirement in order to develop Knowledge-Based systems. In this paper we present Data-Problem-Solver (DPS) approach, a new ontological paradigm that allows the knowledge designer to model and represent a Knowledge Base (KB) for expert systems. Our approach clearly distinguishes among the knowledge about a problem to resolve (answering the “what to do” question), the solver method to resolve it (answering the “how to do” question) and the type of input data required (answering the “what I need” question). The main purpose of the proposed paradigm is to facilitate the generalization of the application domain and the modularity and the expandability of the represented knowledge. The proposed DPS ontological approach is applied to the modelling of the knowledge about a bioinformatics application scenario: the protein complex extraction from a protein-protein interaction network.

关键词： Ontologies Proteins Expert systems bioinformatics Cognition Unified modeling language

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Evolving a social fabric to fit and epidemic profile

Evolving a social fabric to fit and epidemic profile

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Daniel Ashlock Elisabeth Shiller Department of Mathematics and Statistics University of Guelph Guelph ONT Canada

Epidemic models often incorporate contact networks along which the disease can be passed. This study follows up on an earlier one which evolved full general contact networks. This study uses an evolvable network representation inspired by the idea of a social fabric. The resulting representation is based on selecting overlapping groups of agents that interact as if they are well mixed. The groups in this representation are intended to represent groups that are, in fact, well mixed such as schools, families, or workplaces. The new representation permits a substantial improvement in the speed with which a contact model can be fit to an epidemic profile. There is a cost in the form of additional model parameters that must be tuned. A parameter setting study is performed for a simple epidemic profile, providing proof of concept for the evolvable social fabric representation. A number of potential improvements and directions for future work are outlined.

关键词： Fabrics Diseases Social network services Evolutionary computation Educational institutions Contracts computational modeling

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Searching a multicellular model to tame tumor-induced angiogenesis

Searching a multicellular model to tame tumor-induced angiog...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Gregory J. Podgorski Nicholas S. Flann Biology Department & Center for Integrated BioSystems Utah State University

computational modeling has the potential to bridge the knowledge gap between the identification of cellular mechanisms altered in cancer and understanding development of the disease. A multicellular Glazier-Granier-Hogeweg (GGH)-based model that considers biologically realistic parameters of endothelial cells and the extracellular matrix was developed and used to explore how tumors induce angiogenesis. The effect on angiogenesis of modifying these parameters singly and in combination was examined in a massively parallel search to discover novel therapeutic approaches. A two phase search process was employed: First random restart hill climbing identified potentially valuable points within the search space, then multi-dimensional parameter sweeps were performed over the nearby region to further refine solution quality. Examples of combination therapies to block nutrient delivery to the tumor that were discovered in the search are presented. Multicellular modeling to identify potential cancer chemotherapies is a promising approach to reveal new targets for cancer drug development and testing.

关键词： Cancer Tumors computational modeling Standards Medical treatment Blood vessels Electronic countermeasures

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Hybrid feature selection method for biomedical datasets

Hybrid feature selection method for biomedical datasets

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Saúl Solorio-Fernández José Fco. Martínez-Trinidad Jesús Ariel Carrasco-Ochoa Yan-Qing Zhang Computer Science Department National Institute for Astrophysics Optics and Electronics Puebla Mexico Department of Computer Science Georgia State University Atlanta GA USA

Currently classifying high-dimensional data is a very challenging problem. High dimensional feature spaces affect both accuracy and efficiency of supervised learning methods. To address this issue, we present a fast and efficient feature selection algorithm to facilitate classifying high-dimensional datasets as those appearing in bioinformatics problems. Our method employs a Laplacian score ranking to reduce the search space, combined with a simple wrapper strategy to find a good feature subset of uncorrelated features, giving as result a hybrid feature selection method which is useful for high dimensional spaces. Some experiments have been carried out on gene microarray datasets to demonstrate the effectiveness and robustness of the proposed method.

关键词： Accuracy Laplace equations bioinformatics Classification algorithms Machine learning Cancer Filtering algorithms

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Reconstruction and update robustness of the mammalian cell cycle network

Reconstruction and update robustness of the mammalian cell c...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Gonzalo A. Ruz Eric Goles Facultad de Ingeniería y Ciencias Universidad Adolfo Ibañez Santiago Chile

Given the input-output data of the mammalian cell cycle network under a parallel updating scheme, an attempt to construct a threshold Boolean network with the same dynamics is presented. To accomplish this, mutual information is used to find the network structure, then a swarm intelligence optimization technique called the bees algorithm is used to find the weights and thresholds for the network. It is shown that out of the ten regulatory elements (nodes) of the network, only nine can be modeled as a single threshold function, thus, the resulting network is almost a threshold Boolean network with the exception of the CycA protein which remains with its logical rules instead. The robustness of the network is explored with respect to update perturbations, in particular, what happens to the limit cycle attractors when changing from parallel to a sequential updating scheme. Results shows that the network is not robust since different limit cycles of different lengths appear.

关键词： Limit-cycles Robustness Data models Mathematical model computational modeling Vectors Barium

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Quantitative analysis of redundancy in evolution of developmental systems

Quantitative analysis of redundancy in evolution of developm...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Lisa Schramm Yaochu Jin Bernhard Sendhoff Technische Universität Darmstadt Darmstadt Germany Department of Computing University of Surrey Guildford UK Honda Research Institute Europe GmbH Offenbach Germany

Redundancy is believed to play a key role in robustness and evolvability of biological systems. This paper investigates the influence of redundancy on the evolutionary performance of a gene regulatory network governing a cellular growth process. Extensive simulation results suggest that, for the developmental model studied in this work, maintaining sufficient redundancy helps to improve the ability of the evolutionary algorithm to achieve better performance. To examine the change of redundancy during the evolutionary process and its relationship to evolutionary performance, we propose a quantitative definition for measuring different aspects of redundancy, namely, structural redundancy, functional redundancy and functional proximity. Our results show that evolution attempts to increase the functional redundancy after pruning of redundant genes if the evolution is under a larger selection pressure. It is also interesting to notice that an increase in functional proximity enhances the evolutionary performance.

关键词： Redundancy computational modeling Biological system modeling Evolution (biology) Genetics Biological cells

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