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Annual ieee symposium on Foundations of Computer Science

作者： David Doty Jack H. Lutz Matthew J. Patitz Robert T. Schweller Scott M. Summers Damien Woods Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA Computer Science Iowa State University Ames IA USA Computer Science and Computer Engineering University of Arkansas Fayetteville AR USA Computer Science University of Texas Pan-American Edinburg TX USA Computer Science and Software Engineering University of Wisconsin Platteville Platteville WI USA Computer Science California Institute of Technology Pasadena CA USA

We prove that the abstract Tile Assembly Model (aTAM) of nanoscale self-assembly is intrinsically universal. This means that there is a single tile assembly system U that, with proper initialization, simulates any tile assembly system T. The simulation is "intrinsic" in the sense that the self-assembly process carried out by U is exactly that carried out by T, with each tile of T represented by an m × m "super tile" of U. Our construction works for the full aTAM at any temperature, and it faithfully simulates the deterministic or nondeterministic behavior of each T. Our construction succeeds by solving an analog of the cell differentiation problem in developmental biology: Each super tile of U, starting with those in the seed assembly, carries the "genome" of the simulated system T. At each location of a potential super tile in the self-assembly of U, a decision is made whether and how to express this genome, i.e., whether to generate a super tile and, if so, which tile of T it will represent. This decision must be achieved using asynchronous communication under incomplete information, but it achieves the correct global outcome(s).

关键词： Tiles Assembly Self-assembly Assembly systems Genomics bioinformatics computational modeling

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A Preprocessing Procedure for Haplotype Inference by Pure Parsimony

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第5期8卷 1183-1195页

作者： Irurozki, Ekhine Calvo, Borja Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20018 Gipuzkoa Spain

Haplotype data are especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and costly. computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach ( HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved.

关键词： biology and genetics haplotype inference optimization

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Predicting MHC-II Binding Affinity Using Multiple Instance Regression

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第4期8卷 1067-1079页

作者： EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Al Azhar Univ Dept Syst & Comp Engn Cairo Egypt Iowa State Univ Dept Genet Dev & Cell Biol Bioinformat & Computat Biol Grad Program Ames IA 50011 USA Iowa State Univ Artificial Intelligence Lab Bioinformat & Computat Biol Grad Program Dept Comp SciCtr Computat Intelligence Learning Ames IA 50011 USA

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark data sets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://***/mhcmir.

关键词： MHC-II peptide prediction multiple instance learning multiple instance regression

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A bio-inspired agent-based system for controlling robot behaviour

A bio-inspired agent-based system for controlling robot beha...

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ieee symposium on Intelligent Agents

作者： Yao, Y. Baele, G. Van De Peer, Y. Department of Plant Systems Biology VIB B-9052 Ghent Belgium Bioinformatics and Evolutionary Genomics Department of Molecular Genetics Ghent University B-9052 Ghent Belgium

ISBN: (纸本)9781612840604

In this paper, we present an agent-based system to control a single robot's behaviour. We present an artificial genome structure, based on gene regulatory networks, in which several regions can be distinguished such as promoter regions, indicator genes, transcription factor binding sites, regulatory genes and expressed genes. We use agent-based modeling (ABM) to simulate a bio-inspired system based on the artificial genome, with the ultimate goal of providing phenotypic information for a simulated robot. We show that the presence of a feedback loop in the agent-based system, along with the corresponding agent replacements, is essential to allow the robot to perform its tasks. © 2011 ieee.

关键词： Robots

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Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第3期8卷 858-864页

作者： Nguyen, Minh N. Zurada, Jacek M. Rajapakse, Jagath C. BioInfomat Inst Singapore 138671 Singapore Nanyang Technol Univ BioInformat Res Ctr Singapore Singapore Univ Louisville Computat Intelligence Lab Dept Elect & Comp Engn Louisville KY 40292 USA Singapore MIT Alliance Singapore Singapore MIT Dept Biol Engn Cambridge MA 02139 USA

Although numerous computational techniques have been applied to predict protein secondary structure (PSS), only limited studies have dealt with discovery of logic rules underlying the prediction itself. Such rules offer interesting links between the prediction model and the underlying biology. In addition, they enhance interpretability of PSS prediction by providing a degree of transparency to the predicting model usually regarded as a black box. In this paper, we explore the generation and use of C4.5 decision trees to extract relevant rules from PSS predictions modeled with two-stage support vector machines (TS-SVM). The proposed rules were derived on the RS126 data set of 126 nonhomologous globular proteins and on the PSIPRED data set of 1,923 protein sequences. Our approach has produced sets of comprehensible, and often interpretable, rules underlying the PSS predictions. Moreover, many of the rules seem to be strongly supported by biological evidence. Further, our approach resulted in good prediction accuracy, few and usually compact rules, and rules that are generally of higher confidence levels than those generated by other rule extraction techniques.

关键词： Protein structure secondary structure prediction support vector machines multiclass SVM C4.5 decision trees rule extraction

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Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第1期8卷 80-93页

作者： Andonie, Razvan Fabry-Asztalos, Levente Abdul-Wahid, Christopher Badi' Abdul-Wahid, Sarah Barker, Grant I. Magill, Lukas C. Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Chem Ellensburg WA 98926 USA

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR [1] is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. [2], [3]. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.

关键词： Fuzzy neural networks evolutionary computing and genetic algorithms computational chemistry data mining

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ieee CIBCB 2011 Committee

IEEE CIBCB 2011 Committee

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

Provides a listing of current committee members.

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Genetic Networks and Soft Computing

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第1期8卷 94-107页

作者： Mitra, Sushmita Das, Ranajit Hayashi, Yoichi Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Meiji Univ Dept Comp Sci Tama Ku Kawasaki Kanagawa 2148571 Japan

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

关键词： Gene regulatory networks reverse engineering gene expression microarray artificial neural networks genetic algorithms fuzzy sets

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A Biologically Inspired Measure for Coexpression Analysis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第4期8卷 929-942页

作者： Bandyopadhyay, Sanghamitra Bhattacharyya, Malay Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus. As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure, called BioSim, which assumes values between 1 and vertical bar 1 corresponding to negative and positive dependency and 0 for independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it accounts for the features of expression similarity, expression deviation and also the relative dependence. It is demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced measure. By further extending the BioSim measure, it has been shown that one can effectively id

关键词： Coexpression gene similarity correlation gene ontology differential expression

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Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第3期8卷 760-774页

作者： Armananzas, Ruben Saeys, Yvan Inza, Inaki Garcia-Torres, Miguel Bielza, Concha van de Peer, Yves Larranaga, Pedro Univ Politecn Madrid Computat Intelligence Grp Dept Inteligencia Artificial Boadilla Del Monte 28660 Spain Univ Ghent VIB Bioinformat & Syst Biol Grp B-9052 Ghent Belgium Univ Basque Country Dept Comp Sci & Artificial Intelligence Fac Informat Donostia San Sebastian 20018 Spain Pablo de Olavide Univ Area Languages & Comp Syst Seville 41013 Spain

Progress is continuously being made in the quest for stable biomarkers linked to complex diseases. Mass spectrometers are one of the devices for tackling this problem. The data profiles they produce are noisy and unstable. In these profiles, biomarkers are detected as signal regions (peaks), where control and disease samples behave differently. Mass spectrometry (MS) data generally contain a limited number of samples described by a high number of features. In this work, we present a novel class of evolutionary algorithms, estimation of distribution algorithms (EDA), as an efficient peak selector in this MS domain. There is a trade-of f between the reliability of the detected biomarkers and the low number of samples for analysis. For this reason, we introduce a consensus approach, built upon the classical EDA scheme, that improves stability and robustness of the final set of relevant peaks. An entire data workflow is designed to yield unbiased results. Four publicly available MS data sets (two MALDI-TOF and another two SELDI-TOF) are analyzed. The results are compared to the original works, and a new plot (peak frequential plot) for graphically inspecting the relevant peaks is introduced. A complete online supplementary page, which can be found at http://***/ccwbayes/members/ruben/ms, includes extended info and results, in addition to Matlab scripts and references.

关键词： Mass spectrometry EDA feature selection biomarker discovery

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