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ieee International symposium on Biomedical Imaging

作者： Yan Xu Michalis Savelonas Peng Qiu Kristen Trett William Shain Badrinath Roysam Department of Electrical & Computer Engineering University of Houston Houston TX USA Department of Bioinformatics and Computational Biology University of Texas MD Anderson Cancer Center Houston TX USA Center for Integrative Brain Research Seattle Childrens Research Institute Seattle WA USA

ISBN: (纸本)9781467364560

Microglia are Central Nervous System (CNS) cells that are similar to macrophages. They undergo characteristic changes in cell arbor morphology in response to tissue perturbation. Ensembles of microglia exhibit a progression of arbor morphologies. Our goal is to discover these progressions and determine the underlying arbor features from 3-D multi-channel fluorescence confocal microscope images of rat brain tissue multiplex stained with Hoechst to reveal cell nuclei, and immunolabeled for IBA-1 to reveal microglia. The microglia are automatically traced, and a set of 131 arbor features are computed. An agglomerative clustering algorithm based on Pearson's correlation is used to derive coherent modules of features. A k-NNG structural similarity analysis of feature modules enables us to construct a global similarity matrix, from which a global multi-level k-NNG is constructed to derive an interactive progression chart through a modified Fruchterman-Reingold algorithm that clearly reveals a progression from highly ramified microglia to round cells proximal to the injury site of an implanted neural recording device.

关键词： Morphology Context Microscopy Image segmentation Visualization Biomedical imaging Brain

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An efficient machine learning approach to low-complexity filtering in biological sequences

An efficient machine learning approach to low-complexity fil...

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

作者： Barber, Christopher A. Oehmen, Christopher S. Pacific Northwest National Laboratory Richland WA 99352 United States

ISBN: (纸本)9781467311892

Biological sequences contain low-complexity regions (LCRs) which produce superfluous matches in homology searches, and lead to slow execution of database search algorithms such as BLAST. These regions are efficiently identified by low-complexity filtering algorithms such as SDUST and SEG, which are included in the BLAST tool-suite. These algorithms target differing notions of complexity, so an algorithm which combines their sensitivities is pursued. A variety of features are derived from these algorithms, as well as a new filtering algorithm based on Lempel-Ziv complexity. Artificial sequences with known LCRs are used to train and evaluate an SVM classifier, which significantly outperforms the standalone filtering algorithms. © 2012 ieee.

关键词： bioinformatics

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Data Mining Over Biological Datasets: An Integrated Approach Based on computational intelligence

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ieee computational intelligence MAGAZINE 2012年第4期7卷 22-34页

作者： Stegmayer, Georgina Gerard, Matias Milone, Diego H. Natl Sci & Tech Res Council CONICET Ctr Res & Dev Informat Syst CIDISI Buenos Aires DF Argentina Natl Sci & Tech Res Council CONICET Res Ctr Signals Syst & Computat Intelligence Sinc FICH UNL Buenos Aires DF Argentina

biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations;a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters;a validation measure that can assess the biological significance of the clusters found;and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.

关键词： Data mining Medical information processing Genomics bioinformatics Knowledge discovery Informatics Clustering algorithms biology Evolutionary computation

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

作者： Fiannaca, Antonino La Rosa, Massimo Rizzo, Riccardo Urso, Alfonso Gaglio, Salvatore ICAR-CNR National Research Council of Italy Viale delle Scienze Ed. 11 90128 Palermo Italy DICGIM University of Palermo Viale delle Scienze Ed. 6 90128 Palermo Italy

ISBN: (纸本)9781467311892

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. © 2012 ieee.

关键词： Expert systems

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Multiple worlds model for motif discovery

Multiple worlds model for motif discovery

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

作者： Brown, Joseph Alexander School of Computer Science University of Guelph Guelph ON Canada

ISBN: (纸本)9781467311892

In this study we look at a novel evolutionary technique known as the multiple worlds model for unsupervised classification of sequences via evolved motifs. This evolutionary algorithm uses the biological inspirations of species and species extinction as features modeled in the evolution in order to provide classifiers where the number of classes is not known a priori. In the multiple worlds model a number of populations which do not interbreed compete in fitness evaluation. Sequence motifs are small, biologically significant DNA/RNA or amino acid segments. They are represented as strings of symbols and wild cards. The model works well to locate classification motifs for sequences whose classes have statistical deviations such as those with a GC content difference or those created from differing Self-Driving Markov models. The creation of such classifiers will allow biologists to examine large sets of sequences in order to discover significant features. © 2012 ieee.

关键词： bioinformatics

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Modeling atherosclerotic plaque growth: A case report based on a 3D geometry of left coronary arterial tree from computed tomography

Modeling atherosclerotic plaque growth: A case report based ...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Antonis I. Sakellarios Panagiotis K. Siogkas Lambros S. Athanasiou Themis P. Exarchos Michail I. Papafaklis Christos V. Bourantas Katerina K. Naka Dimitra Iliopoulou Lampros K. Michalis Nenad Filipovic Oberdan Parodi Dimitrios I. Fotiadis Dept. of Materials Science and Engineering University of Ioannina GR Dept. of Biomedical Research FORTH Institute of Molecular Biology and Biotechnology Ioannina Greece Cardiovascular Division Harvard Medical School Boston USA Dept. of Interventional Cardiology Erasmus MC Thoraxcenter Rotterdam The Netherlands Dept. of Cardiology in Medical School University of Ioannina Ioannina GR Greece institute of communication and computer systems University of Kragujevac Serbia National Research Council Institute of Clinical Physiology Pisa Italy

In this study, we present an innovative model for plaque growth utilizing a 3-Dimensional (3D) left coronary arterial tree reconstructed from computed tomographic (CT) data. The proposed model takes into consideration not only the effect of the local hemodynamic factors but also major biological processes such as the low density lipoprotein (LDL) and high density lipoprotein (HDL) transport, the macrophages recruitment and the foam cells formation. The endothelial membrane is considered semi-permeable and endothelial shear stress dependent, while its permeability is modeled using the Kedem-Katscalsky equations. Patient specific biological data are used for the accurate modeling of plaque formation process. The finite element method (FEM) is employed for the solution of the system of partial differential equations. The results of the simulation are compared to the plaque progression in a follow-up CT examination performed three years after the initial investigation. The results show that the proposed model can be used to predict regions prone for plaque development of progression.

关键词： Mathematical model Arteries computational modeling Hardware design languages Atherosclerosis Computed tomography Equations

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Breeding Value Estimate based on Support Vector Machine

Breeding Value Estimate based on Support Vector Machine

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5th International symposium on computational intelligence and Design (ISCID)

作者： Hong, Shibao Hu, Yaguang Wan, Shanhong Zhang, Defu Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China

ISBN: (纸本)9780769548111

Breeding value is the sum of additive effects of all the genes, which has important effect on breeding. Statistical methods are often used traditionally to estimate breeding value. This paper uses support vector regression machine to estimate breeding value, and two feature-selection methods are developed to preprocess the data. The proposed algorithm is compared with statistical methods in other literatures, its performance is verified.

关键词： estimate breeding value support vector machine feature selection predict bioinformatics

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Protein secondary structure prediction using BLAST and exhaustive RT-RICO, the search for optimal segment length and threshold

Protein secondary structure prediction using BLAST and exhau...

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

作者： Lee, Leong Leopold, Jennifer L. Frank, Ronald L. Department of Computer Science Austin Peay State University Clarksville TN United States Department of Computer Science Missouri University of Science and Technology Rolla MO United States Department of Biological Sciences Missouri University of Science and Technology Rolla MO United States

ISBN: (纸本)9781467311892

Protein secondary structure prediction from its amino acid sequence is a well studied computational problem in bioinformatics and data mining. It can be viewed as an intermediate research objective to solving the more challenging protein three-dimensional structure prediction problem, which is one of the most important research goals of bioinformatics. Although the secondary structure prediction problem was first defined in the 1960s, the prediction accuracy of the most modern methods still hovers around 80%. In [1] this research team presented a protein secondary structure prediction method, BLAST-RT-RICO (Relaxed Threshold Rule Induction from Coverings), that employs a modified association rule learning approach, utilizing multiple sequence alignment information, to predict secondary structures. Despite producing higher prediction accuracy than many other contemporary methods, that preliminary research study identified some crucial areas in need of improvements, such as determining the optimal segment length, finding the optimal threshold value, and improving the time complexity for the rule generation algorithm. In this paper, we present a modified method, BLAST-ERT-RICO (Exhaustive Relaxed Threshold Rule Induction from Coverings), which has an improved time complexity, as well as more optimal choices of segment length and threshold value. Preliminary test results showed that with a segment length of 9 amino acid residues, and a threshold value of 0.8, BLAST-ERT-RICO achieved a Q3 score of 92.19% on the standard test dataset RS126, which suggests that this approach may be even more useful as a secondary structure prediction method in the future. © 2012 ieee.

关键词： bioinformatics

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Signal-processing-based bioinformatics approach for the identification of influenza A virus subtypes in Neuraminidase genes

Signal-processing-based bioinformatics approach for the iden...

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Annual International Conference of the ieee Engineering in Medicine and biology Society

作者： Charalambos Chrysostomou Huseyin Seker Department of Genetics University of Leicester Bio-Health Informatics Research Group Centre for Computational Intelligence Faculty of Technology De Montfort University

ISBN: (纸本)9781457702150

Neuraminidase (NA) genes of influenza A virus is a highly potential candidate for antiviral drug development that can only be realized through true identification of its sub-types. In this paper, in order to accurately detect the sub-types, a hybrid predictive model is therefore developed and tested over proteins obtained from the four subtypes of the influenza A virus, namely, H1N1, H2N2, H3N2 and H5N1 that caused major pandemics in the twentieth century. The predictive model is built by the following four main steps;(i) decoding the protein sequences into numerical signals by means of EIIP amino acid scale, (ii) analysing these signals (protein sequences) by using Discrete Fourier Transform (DFT) and extracting DFT-based features, (iii) selecting more influential sub-set of the features by using the F-score statistical feature selection method, and finally (iv) building a predictive model on the feature sub-set by using support vector machine classifier. The protein sequences were chosen as to be of high percentage identity that they demonstrate within individual influenza subtype classes and high variation that they display in the percentage identity. This makes the proteins very difficult to distinguish from each other even they belong to different subtypes. Given this set of the proteins, the predictive model yielded 98.3% accuracy based on a 5-fold cross validation. This also results in a twenty feature sub-set that can also help reveal spectral characteristics of the subtypes. The proposed model is promising and can easily be generalized for other similar studies.

关键词： Amino Acid Indices Discrete Fourier Transform (DFT) F-score Neuraminidase Genes Support Vector Machines

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A Reinforcement Learning Approach for Solving the Fragment Assembly Problem

A Reinforcement Learning Approach for Solving the Fragment A...

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13th International symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Bocicor, Maria-Iuliana Czibula, Gabriela Czibula, Istvan-Gergely Univ Babes Bolyai Dept Comp Sci 1 M Kogalniceanu St Cluj Napoca 400084 Romania

ISBN: (纸本)9780769546308

The DNA fragment assembly is a very complex optimization problem important within many fields including bioinformatics and computational biology. The problem is NP-hard, that is why many computational techniques including computational intelligence algorithms were designed for finding good solutions for this problem. Since DNA fragment assembly is a crucial part of any sequencing project, researchers are still focusing on developing better assemblers. In this paper we aim at proposing a new reinforcement learning based model for solving the fragment assembly problem. We are particularly focusing on the DNA fragment assembly problem. Our model is based on a Q-learning agent-based approach. The experimental evaluation confirms a good performance of the proposed model and indicates the potential of our proposal.

关键词： bioinformatics reinforcement learning DNA fragment assembly

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