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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

The use of covariance models in finding non-coding RNA gene members in genome sequence databases has been shown quite effective in many studies. However, it has a significant drawback, which is the very large computational burden. A combined covariance model is proposed to reduce the search complexity when a genome sequence is searched for more than one ncRNA gene family. The covariance models that are combined are selected using a hierarchical clustering algorithm. This study shows that when a small number of original covariance models are combined, the combined covariance model can find members from all original ncRNA families thus successfully reducing the search time.

关键词： RNA bioinformatics Genomics Hidden Markov models Databases computational modeling Periodic structures

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Regularized linear discriminant analysis and its recursive implementation for gene subset selection

Regularized linear discriminant analysis and its recursive i...

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

作者： K. Z. Mao Feng Yang Wenyin Tang School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Although mostly used for pattern classification, linear discriminant analysis (LDA) may also be used for feature selection. When employed to select genes for microarray data, which has high dimensionality and small sample size, LDA encounters three problems, including singularity of scatter matrix, overfitting and prohibitive computational complexity. In this study, we propose a new regularization technique to address the singularity and overfitting problem. In addition, we develop a recursive implementation for LDA to reduce computational overhead. Experimental studies on 5 gene microarray problems show that the regularized linear discriminant analysis (RLDA) and its recursive implementation produce gene subsets with excellent classification performance.

关键词： Error analysis Cancer Linear discriminant analysis computational complexity Training data Colon

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Microarray Time Course Experiments: Finding Profiles

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第2期8卷 464-475页

作者： Irigoien, Itziar Vives, Sergi Arenas, Concepcion Univ Basque Country Dept Computat Sci & Artificial Intelligence Donostia San Sebastian 20080 Spain Univ Barcelona Dept Stat E-08028 Barcelona Spain

Time course studies with microarray techniques and experimental replicates are very useful in biomedical research. We present, in replicate experiments, an alternative approach to select and cluster genes according to a new measure for association between genes. First, the procedure normalizes and standardizes the expression profile of each gene, and then, identifies scaling parameters that will further minimize the distance between replicates of the same gene. Then, the procedure filters out genes with a flat profile, detects differences between replicates, and separates genes without significant differences from the rest. For this last group of genes, we define a mean profile for each gene and use it to compute the distance between two genes. Next, a hierarchical clustering procedure is proposed, a statistic is computed for each cluster to determine its compactness, and the total number of classes is determined. For the rest of the genes, those with significant differences between replicates, the procedure detects where the differences between replicates lie, and assigns each gene to the best fitting previously identified profile or defines a new profile. We illustrate this new procedure using simulated data and a representative data set arising from a microarray experiment with replication, and report interesting results.

关键词： Cluster analysis typical unit gene profile time course experiment replicate

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A Genetic Optimization Approach for Isolating Translational Efficiency Bias

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第2期8卷 342-352页

作者： Raiford, Douglas W. Krane, Dan E. Doom, Travis E. W. Raymer, Michael L. Univ Montana Dept Comp Sci Missoula MT 59812 USA Wright State Univ Dept Biol Sci Dayton OH 45435 USA Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.

关键词： Codon usage bias evolutionary computing and genetic algorithms miscellaneous artificial intelligence computing methodologies GC-content strand bias translational efficiency

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Parallel algorithms for Bayesian networks structure learning with applications to systems biology

Parallel algorithms for Bayesian networks structure learning...

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25th ieee International Parallel and Distributed Processing symposium, Workshops and Phd Forum, IPDPSW 2011

作者： Nikolova, Olga Bioinformatics and Computational Biology Department of Computer Engineering Iowa State University Ames IA United States

ISBN: (纸本)9780769543857

Bayesian networks (BN) are probabilistic graphical models which are widely utilized in modeling complex biological interactions in the cell. Learning the structure of a BN is an NP-hard problem and existing exact and heuristic solutions do not scale to large enough domains to allow for meaningful modeling of many biological processes. In this work, we present efficient parallel algorithms which push the scale of both exact and heuristic BN structure learning. We demonstrate the applicability of our methods by implementations on an IBM Blue Gene/L and an AMD Opteron cluster, and discuss their significance for future applications to systems biology. © 2011 ieee.

关键词： Bayesian networks

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An algorithm for self-organized aggregation of swarm robotics using timer

An algorithm for self-organized aggregation of swarm robotic...

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ieee symposium on Swarm intelligence

作者： Yan, Xinan Liang, Alei Guan, Haibing School of Software Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China Department of Computer Science Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781612840529

Aggregation is a basic collective behavior in biology, and is a prerequisite for many applications of swarm robotics. This paper proposes a new distributed algorithm for aggregation of swarm robotics under the constraints of no central control, no information about positions, and only local interaction among robots. Our control strategy contains two states, Search and Wait, for individual robot, as in the model of probabilistic finite state automata (PFSA). The main difference from PFSA is the way of how individual robot decides to leave from an aggregate. In our approach, each robot in an aggregate has a timer as its lifetime in the aggregate instead of having a leaving probability in PFSA. Further, to make all robots aggregate together, the key idea of our algorithm is that robots in small aggregate have shorter lifetime than those in large aggregate. The lifetime of individual robot in an aggregate is approximated as the lifetime of the aggregate, by making all the timers of the robots in the same aggregate have the same time settings, where the time is proportional to aggregate size and is updated only with the increase of aggregate size. By choosing linear function as the time setting of the timer, experiments based on simulator have been done. The results show that our algorithm is successful and scalable for large scale of robots, and indicate that the performance of the aggregation of all robots is improved by decreasing the lifetime of uncompleted aggregate in high robot density or increasing it in low robot density. © 2011 ieee.

关键词： Swarm intelligence

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Discovery of MicroRNA markers: An SVM-based multiobjective feature selection approach

Discovery of MicroRNA markers: An SVM-based multiobjective f...

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

作者： Anirban Mukhopadhyay Ujjwal Maulik Sanghamitra Bandyopadhyay Department of Computer Science and Engineering University of Kalyani Kalyani India Department of Computer Science and Engineering Jadavpur University Kolkata India Machine Intelligence Unit Indian Statistical Institute Kolkata India

MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting promising miRNAs having differential expression in benign and malignant tissue samples. Subsequently, the non-dominated sets of promising miRNAs are aggregated into a single most promising miRNA subset. Finally, the Signal-to-Noise Ratio (SNR) statistic has been applied on the obtained miRNA subset for identifying potential miRNA markers that distinguish the two classes (benign and malignant) of tissue samples. The performance has been demonstrated on four real-life miRNA expression datasets for different SVM kernel functions and the identified miRNA markers are reported.

关键词： Support vector machines Cancer Kernel Signal to noise ratio Optimization Genetic algorithms Colon

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Computing the tree of life: Leveraging the power of desktop and service grids

Computing the tree of life: Leveraging the power of desktop ...

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25th ieee International Parallel and Distributed Processing symposium, Workshops and Phd Forum, IPDPSW 2011

作者： Bazinet, Adam L. Cummings, Michael P. Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 United States

ISBN: (纸本)9780769543857

The trend in life sciences research, particularly in molecular evolutionary systematics, is toward larger data sets and ever-more detailed evolutionary models, which can generate substantial computational loads. Over the past several years we have developed a grid computing system aimed at providing researchers the computational power needed to complete such analyses in a timely manner. Our grid system, known as The Lattice Project, was the first to combine two models of grid computing - the service model, which mainly federates large institutional HPC resources, and the desktop model, which harnesses the power of PCs volunteered by the general public. Recently we have developed a "science portal" style web interface that makes it easier than ever for phylogenetic analyses to be completed using GARLI, a popular program that uses a maximum likelihood method to infer the evolutionary history of organisms on the basis of genetic sequence data. This paper describes our approach to scheduling thousands of GARLI jobs with diverse requirements to heterogeneous grid resources, which include volunteer computers running BOINC software. A key component of this system provides a priori GARLI runtime estimates using machine learning with random forests. © 2011 ieee.

关键词： Phylogenetic

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Tackling the challenging motif problem through hybrid particle swarm optimized alignment clustering

Tackling the challenging motif problem through hybrid partic...

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

作者： Chengpeng Bi Division of Clinical Pharmacology Childrens Mercy Hospitals and Clinics School of Medicine School of Computing University of Missouri Kansas City MO USA

Previous studies show that Gibbs sampling methods and the like desperately failed to solve the challenging motif problem. This paper proposes a new hybrid algorithm, integrated Gibbs with particle swarm optimization (PSO) based motif alignment clustering (PSO-MAC), to solve the challenging motif problem by iteratively refining a population of potential solutions. The PSO-MAC algorithm is closely incorporated into a variant Gibbs called pseudo-Gibbs (pGibbs) motif sampler. Notably, pGibbs as a forerunner is executed multiple times and hence it brings about a population of potential alignments. Then, a PSO procedure coupled with motif alignment clustering (MAC) is developed to fine-tune such a population of solutions. The hybrid PSO-MAC algorithm aims to glean high quality motif solutions by cyclically refining and clustering the solution pool. Simulation and experimental results show that the new hybrid algorithm performs markedly better than others tested, and surprisingly it is able to solve the challenging motif problem with high precision. The new hybrid algorithm is also successfully applied to large-scale ChIP-Seq data sets.

关键词： Clustering algorithms Prediction algorithms Pulse width modulation Particle swarm optimization DNA bioinformatics Stochastic processes

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A simulated annealing algorithm for resource allocation and scheduling with precedence constraints in the GLECLUBS/eGLECLUBS pipelines

A simulated annealing algorithm for resource allocation and ...

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14th ieee Int. Conf. on computational Science and Engineering, CSE 2011, the 11th International symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2011, and the 10th ieee Int. Conf. on Ubiquitous Computing and Communications, IUCC 2011

作者： Zhang, Shaoqiang Sun, Huazhi Li, Guojun Su, Zhengchang College of Computer and Information Engineering Tianjin Normal University Tianjin 300387 China Department of Biochemistry and Molecular Biology Institute of Bioinformatics University of Georgia GA 30602 United States School of Mathematics Shandong University Jinan 250100 China Department of Bioinformatics and Genomics University of North Carolina at Charlotte Charlotte NC 28233 United States

ISBN: (纸本)9780769544779

We present a heuristic algorithm for solving a series of resource allocation and scheduling problems. Given a resource of fixed size in a system, the problems are to schedule jobs with resource requirements, release and due dates, and priorities, so as to maximize the throughput, make span, or average completion time. The models have a wide range of applications such as bandwidth allocation for sessions in communication networks and resource reservations in cluster computing systems. Particularly, in order to speed up two bioinformatic pipelines "GLECLUBS" and "eGLECLUBS" we designed recently, which are used to predict cis-regulatory motifs in a large scale of prokaryotic genomes simultaneously, we employ the heuristic to schedule the packages in the pipelines for a genome or a group of genomes. Our heuristic algorithm is simple, efficient, and based on simulated annealing. © 2011 ieee.

关键词： Heuristic algorithms

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