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5th International symposium on Intelligent Data Analysis, IDA 2003

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5th International symposium on Intelligent Data Analysis, IDA 2003

ISBN: (纸本)3540408134

The proceedings contain 56 papers. The special focus in this conference is on Machine Learning, Probability and Topology. The topics include: Pruning for monotone classification trees;regularized learning with flexible constraints;learning to answer emails;a semi-supervised method for learning the structure of robot environment interactions;using domain specific knowledge for automated modeling;resolving rule conflicts with double induction;a novel partial-memory learning algorithm based on grey relational structure;constructing hierarchical rule systems;text categorization using hybrid multiple model schemes;learning dynamic bayesian networks from multivariate time series with changing dependencies;topology and intelligent data analysis;coherent conditional probability as a measure of information of the relevant conditioning events;very predictive ngrams for space-limited probabilistic models;learning linear classifiers sensitive to example dependent and noisy costs;an effective associative memory for pattern recognition;similarity based classification;numerical attributes in decision trees;similarity-based neural networks for applications in computational molecular biology;combining pairwise classifiers with stacking and adapting association rule learning to subgroup discovery.

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Gaussian mixture density estimation applied to microarray data

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5th International symposium on Intelligent Data Analysis, IDA 2003

作者： Steinhoff, Christine Müller, Tobias Nuber, Ulrike A. Vingron, Martin Max-Planck-Institute for Molecular Genetics Computational Mol. Biology Human Molecular Genetics Ihnestraße 63–73 BerlinD-14195 Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland WürzburgD-97074 Germany

ISBN: (纸本)3540408134

Several publications have focused on fitting a specific distribution to overall microarray data. Due to a number of biological features the distribution of overall spot intensities can take various shapes. It appears to be impossible to find a specific distribution fitting all experiments even if they are carried out perfectly. Therefore, a probabilistic representation that models a mixture of various effects would be suitable. We use a Gaussian mixture model to represent signal intensity profiles. The advantage of this approach is the derivation of a probabilistic criterion for expressed and non-expressed genes. Furthermore our approach does not involve any prior decision on the number of model parameters. We properly fit microarray data of various shapes by a mixture of Gaussians using the EM algorithm and determine the complexity of the mixture model by the Bayesian Information Criterion (BIC). Finally, we apply our method to simulated data and to biological data. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Gaussian distribution

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Instrumentation for genome analysis (and beyond) based on the TI Digital Micromirror Device

Instrumentation for genome analysis (and beyond) based on th...

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Annual International symposium on Reliability Physics

作者： H.R. Garner University of Texas Southwestern Medical Center Dallas Dallas TX USA

The author presents an overview of various MEMS applications in the biological and medical research field. Specific examples use the Digital Micromirror Device (DMD) developed by Texas Instruments. The University of Texas Southwestern Medical Center develops instrumentation and computational biology tools to exploit the emerging biomedical databases. We apply these tools to problems in biology, genetics, genomics, and medicine. The field of bioMEMS merges biology, physics, mathematics, engineering, computer science and clinical medicine.

关键词： Instruments Genomics bioinformatics Micromirrors Micromechanical devices computational biology Databases Genetics Physics Mathematics

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An immune co-evolutionary algorithm for n-th agent's traveling salesman problem

An immune co-evolutionary algorithm for n-th agent's traveli...

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ieee International symposium on computational intelligence in Robotics and Automation (CIRA)

作者： N. Toma S. Endo K. Yamada Graduate Student Doctoral Course in Complex Intelligent Systems Engineering Graduate School of Science and Engineering University of Ryukyus Japan Department of Information Engineering Faculty of Engineering University of Ryukyus Japan

The purpose of this paper is to evaluate an immune optimization algorithm using a biological immune co-evolutionary phenomenon and cell-cooperation. The co-evolutionary model searches solutions through the interactions between two kinds of agents, on the agent is called immune agent, which optimizes the cost of its own work. The other is call antigen agent, which realized the equal work assignment. This algorithm solves the division-of-labor problems in multi-agent system (MAS) through the three kinds of interactions: division-and-integration processing is used for optimization of the work-cost of immune agents and immune cell-cooperation is used to perform equal work assignment as a result of evolving the antigen agents. To investigate the validity, this algorithm is applied to "n-th agent's traveling salesman problem" as a typical problem of MAS. The good property on solving for MAS will be clarified by some simulations.

关键词： Traveling salesman problems Immune system Biological system modeling Cost function Robust stability Intelligent systems Competitive intelligence Systems engineering and theory Roentgenium biology

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Requirements of phylogenetic databases

Requirements of phylogenetic databases

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： L. Nakhleh D. Miranker F. Barbancon Department of Computer Sciences University of Texas at Austin Austin TX

We examine the organizational impact on phylogenetic databases of the increasing sophistication in the need and use of phylogenetic data. A primary issue is the use of the unnormalized representation of phylogenies in Newick format as a primitive data type in existing phylogenetic databases. In particular we identify and enumerate a list of potential applications of such databases and queries (use-cases) that biologists may wish to see integrated into a phylogenetic database management system. We show there are many queries that would best be supported by a normalized data model where phylogenies are stored as lists of edges. Since many of the queries require transitive traversals of the phylogenies we demonstrate, constructively, that complex phylogenetic queries can be conveniently constructed as Datalog programs. We address concerns with respect to the cost and performance of the normalized representation by developing and empirically evaluating a feasibility prototype.

关键词： Phylogeny History computational biology bioinformatics Evolution (biology) Relational databases biology computing Environmental factors Database systems Data models

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computational model of the role of deficit-related drives in sequential movement learning in a T-maze environment

Computational model of the role of deficit-related drives in...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： W. Li J.D. Johnson Bioengineering Department University of Tolctlo and Eötvös Lárand University Toledo OH USA University of Toledo Toledo OH US Dept. of Bioeng. Toledo Univ. OH USA

We present a computational model of approach teaming in a T-maze environment. We show that our model learns the correct sequence of six decisions that lead to the location of positive reinforcement and in a manner consistent with experimental observations. Our model exhibits many properties that are characteristic of animal learning in maze environments including delay conditioning, secondary conditioning, and backward chaining. Our model incorporates a comprehensive definition of drive that consists of a primary drive (food) and deficit-related signal (hunger), and an acquired drive (the learned expectation for future reward or punishment). In the T-maze environment, the deficit-related drive of hunger motivates the teaming system to search for food. After several trials in the T-maze, the acquired drive (learned expectation) will shape the teaming system's behavior and allow it to consistently find the food. We propose that changes in drive level, not merely the level of the drive, lead to teaming. Positive changes in drive level results in the enhanced behavior and negative changes result in the depressed behavior. Our comprehensive definition of drive allows us to explain teaming in a biologically plausible manner and is supported by results from hypertension, obesity, and Parkinson's disease research.

关键词： computational modeling Biomedical engineering Animals Delay Learning systems Drives Hypertension Parkinson's disease biology computing Biological system modeling

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A fast algorithm for RNA secondary structure prediction including pseudoknots

A fast algorithm for RNA secondary structure prediction incl...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： F. Tahi S. Engelen M. Regnier La.M.I.-UMR 8042 CNRS/Universite dEvry-Val dEssonne/Genopole Evry France INRIA Rocquencourt Le Chesnay France

Many important RNA molecules contain pseudoknots, which are generally excluded by the definition of the secondary structure, mainly for computational reasons. Still, most existing algorithms for secondary structure prediction are not satisfactory in results and complexities, even when pseudoknots are not allowed. We present an algorithm, called P-DCFold, for the prediction of RNA secondary structures including all kinds of pseudoknots. It is based on the comparative approach. The helices are searched recursively, from more "likely" to less "likely", using the "Divide and Conquer" approach. This approach, which allows to limit the amount of searching, is possible when only non-interleaved helices are searched for. The pseudoknots are therefore searched in several steps, each helix of the pseudoknot being selected in a different step. P-DCFold has been applied to tmRNA and RnaseP sequences. In less than two seconds, their respective secondary structures, including their pseudoknots, have been recovered very efficiently.

关键词： RNA Evolution (biology) Robustness Switches Biological control systems Databases Predictive models Context modeling Stochastic processes bioinformatics

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BARD: a visualization tool for biological sequence analysis

BARD: a visualization tool for biological sequence analysis

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ieee symposium on Information Visualization (INFOVIS)

作者： R. Spell R. Brady F. Dierich Department of Biomedical Engineering Duke University Duke University Pratt School of Engineering

We present BARD (biological arc diagrams), a visualization tool for biological sequence analysis. The development of BARD began with the application of Wattenberg's arc diagrams (Wattenberg, 2002) to results from sequence analysis programs, such as BLAST (Atschul et al., 1990). In this paper, we extend the initial arc diagram concept in two ways: 1) by separating the visualization method from the underlying matching algorithm and 2) by expanding the types of matches to include inexact matches, complemented palindrome matches, and inter-sequence matches. BARD renders each type of match distinctly, resulting in a powerful tool to quickly understand sequence similarities and differences. We illustrate the power of BARD by applying the technique to a comparative sequence analysis of the human pathogenic fungi Cryptococcus neoformans.

关键词： Sequences bioinformatics Genomics biology computing Data visualization Organisms Biomedical engineering Genetics Humans computational biology

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Towards automated derivation of biological pathways using high-throughput biological data

Towards automated derivation of biological pathways using hi...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Yu Chen Trupti Joshi Ying Xu Dong Xu ORNL-UT Graduate School of Genome Science and Technology Oak Ridge TN USA Computer Sciences and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)0769519075

Characterizing biological pathways at the genome scale is one of the most important and challenging tasks in the post genomic era. To address this challenge, we have developed a computational method to systematically and automatically derive partial biological pathways in yeast using high-throughput biological data, including yeast two hybrid data, protein complexes identified from mass spectroscopy, genetics interactions, and microarray gene expression data in yeast Saccharomyces cerevisiae. The inputs of the method are the upstream starting protein (e.g., a sensor of a signal) and the downstream terminal protein (e.g., a transcriptional factor that induces genes to respond the signal); the output of the method is the protein interaction chain between the two proteins. The high-throughput data are coded into a graph of interaction network, where each node represents a protein. The weight of an edge between two nodes models the "closeness" of the two represented proteins in the interaction network and it is defined by a rule-based formula according to the high-throughput data and modified by the protein function classification and subcellular localization information. The protein interaction cascade pathway in vivo is predicted as the shortest path identified from the graph of the interaction network using Dijkstra's algorithm. We have also developed a web server of this method (http://***/structure/pathway) for public use. To our knowledge, our method is the first automated method to generally construct partial biological pathways using a suite of high-throughput biological data. This work demonstrates the proof of principle using computational approaches for discoveries of biological pathways with high-throughput data and biological annotation data.

关键词： Biological information theory Fungi Genomics bioinformatics biology computing Protein engineering Mass spectroscopy Genetics Gene expression Biosensors

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A coding and control mechanism of natural computation

A coding and control mechanism of natural computation

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ieee International symposium on Intelligent Control (ISIC)

作者： Tao Gong Jingfeng Cai Zixing Cai College of Information Science and Engineering Central South University Changsha China Department of Electrical and Computer Engineering University of Akron Akron OH USA College of Information Science and Engjneering Central South University Changsha China

In this paper, a frame of natural computation has been proposed in order to deal with the challenge from new computing branches such as DNA computation and quantum computation to traditional computing theory, and computation is considered as the inherent mechanism of the nature and the foundation of intelligence. Simulating computation has been created and improved via inspiration of natural computation, and Artificial intelligence has been established on simulation of human brain computing pattern. According to this view, generalized mapping from computing pattern existing in the nature to current burgeoning new computing pattern inspired by the nature has been modeled, and then a coding and control mechanism of natural computation has been analyzed with formalizing tools. So it can be concluded the theory foundation, intelligent power, and application frame will improve the sciences, such as computer science, artificial intelligence etc.

关键词： biology computing Quantum computing DNA computing Optical computing computational modeling Artificial intelligence Brain modeling Computer simulation Machine intelligence Computer science

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