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A Systems biology Approach to Solving the Puzzle of Unknown Genomic Gene-Function Association Using Grid-Ready SVM Committee Machines

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ieee computational intelligence MAGAZINE 2012年第4期7卷 46-54页

作者： Lee, Tsung-Lu Michael Chiang, Jung-Hsien Kun Shan Univ Tainan Taiwan Natl Cheng Kung Univ Tainan 70101 Taiwan

Genomic researchers face the common challenge of deriving the functions of genes and proteins from high-throughput data. Experimental validation of protein function is costly and time-consuming. With the increased effectiveness of computational intelligence approaches, researchers aim to target the problem with in silico prediction of protein interactions and functions. We propose a systems biology approach that consists of machine-learning and visualization intelligence and aims to predict protein-protein interactions and enhance protein function annotation. Our machine-learning intelligence, SVM committee machines, is compatible with grid computing and large-scale data analysis. In this paper, we not only elucidate the computational power of protein interactions prediction, but also aim to emphasize the interpretation of protein function annotation through protein interaction network analysis.

关键词： Genomics bioinformatics Genetics Proteins Biological system modeling Informatics

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An integrative bioinformatics approach for identifying subtypes and subtype-specific drivers in cancer

An integrative bioinformatics approach for identifying subty...

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

作者： Chen, Peikai Hung, Y.S. Fan, Yubo Wong, Stephen T.-C. University of Hong Kong Hong Kong Hong Kong Methodist Hospital Research Institute Houston TX 77030 United States

ISBN: (纸本)9781467311892

Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. © 2012 ieee.

关键词： Diseases

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PresentaBALL — A powerful package for presentations and lessons in structural biology

PresentaBALL — A powerful package for presentations and les...

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ieee symposium on Biological Data Visualization (BioVis)

作者： Stefan Nickels Daniel Stöckel Sabine C. Mueller Hans-Peter Lenhof Andreas Hildebrandt Anna Katharina Dehof Intel Visual Computing Institute Saarland University Saarbrücken Germany Center for Bioinformatics Saarland University Saarbrücken Germany Universitat des Saarlandes Saarbrucken Saarland DE

Structural biology is based on an important observation: the function of a biomolecule is determined by its three-dimensional structure and its physico-chemical properties. Hence, visualization, modeling, and simulation of molecular structures and of their properties are crucial tools of the field. Typically, the graphical interfaces to molecular modeling packages are aimed at domain experts with significant experience and require an extensive learning period. But in many scenarios, such as teaching, presentations, and demonstrations, it would be highly preferable to have an intuitive environment for showcasing molecular functionality. Ideally, it should support simple preparation of the presentations as well as their convenient display. To keep the user interface simple and focused, the environment should be particularly adapted to the processing of molecular structures. Here, we present such a presentation framework, called PresentaBALL, which uses established web technology standards to provide a freely configurable browser-based interface into the extensive modeling and visualization capabilities of the Biochemical Algorithms Library (BALL). The web interface is embedded into BALL's graphical frontend BALLView, and provides complete, interactive access to the loaded molecular data. PresentaBALL enables researchers in biology with basic knowledge in HTML, JavaScript, or Python to easily setup academic tutorials, demonstrations, or scientific presentations and lectures with 3D structure content and interactive workflows. Owing to its flexible design, other modern forms of teaching and presentation, such as massive open online courses (MOOC) can also use PresentaBALL as their core component. PresentaBALL is licensed under the GNU Public License (GPL) and will be made available in BALL/BALLView, starting with the upcoming release (1.5).

关键词： Visualization Data visualization Education Proteins computational modeling HTML

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Robust cell segmentation for schizosaccharomyces pombe images with focus gradient

Robust cell segmentation for schizosaccharomyces pombe image...

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ieee International symposium on Biomedical Imaging

作者： Jyh-Ying Peng Yen-Jen Chen Marc D. Green Susan L. Forsburg Chun-Nan Hsu Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Education and Research Taipei City Hospital Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Molecular and Computational Biology Program University of Southern California Los Angeles CA USA Information Sciences Institute University of Southern California Marina del Rey CA USA

ISBN: (纸本)9781467364560

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images, the first step of which requires robust segmentation of the cell. We developed a segmentation system, PombeX, that can segment cells from transmitted illumination images with focus gradient and varying contrast. Corrections for focus gradient are applied to the image to accurately detect cell membrane and cytoplasm pixels. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of different image quality, lighting condition, focus condition and phenotypic profile. Comparisons with recent related methods show that PombeX outperforms current methods for fission yeast cell image segmentation.

关键词： Image segmentation Lighting Robustness Proteins Genomics bioinformatics Image edge detection

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Unsupervised inference of arbor morphology progression for microglia from confocal microscope images

Unsupervised inference of arbor morphology progression for m...

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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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