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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology"

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Parallel processing of approximate sequence matching using disk-based suffix tree on multi-core CPU

Parallel processing of approximate sequence matching using d...

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ieee International Workshop on computational intelligence & Applications (IWCIA)

作者： Yosuke Watanuki Keiichi Tamura Hajime Kitakami Yoshihumi Takahashi Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

Suffix trees, which are trie structures that present the suffixes of given sequences (e.g., strings), are widely used for sequence search in different application domains such as, text data mining, web intelligence, bioinformatics and computational biology. In particular, suffix trees are useful in bioinformatics applications, because they can search similar sub-sequences and extract frequent sequence patterns efficiently. In recent years, efficient construction of a suffix tree that allows faster sequence searches has become one of the most important challenges, because the number and size of the data that are stored in sequence databases have been increasing exponentially. This paper proposes a novel parallelization model for approximate sequence matching that uses disk-based suffix trees, which are built on hard disks not on memory, on a multi-core CPU. In the proposed parallelization model, we divide an entire sequence database into two or more sub-databases called partitions. For each partition, we build a suffix tree and define a task as an approximate sequence matching on one suffix tree. Moreover, the proposed parallelization model involves a multiple buffering management system to avoid conflicts among CPU-cores. We evaluated the proposed parallelization model using an actual amino acid sequence database on a PC. The experimental results show a substantial improvement in computation performance.

关键词： Instruction sets Hard disks Parallel processing Indexes bioinformatics Amino acids

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Classifying aging genes into DNA repair or non-DNA repair-related categories 1

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9th International Conference on Intelligent Computing, ICIC 2013

作者： Fang, Yaping Wang, Xinkun Michaelis, Elias K. Fang, Jianwen Applied Bioinformatics Laboratory University of Kansas Lawrence KS 66047 United States Department of Pharmacology and Toxicology University of Kansas Lawrence KS 66047 United States Higuchi Biosciences Center University of Kansas Lawrence KS 66047 United States

ISBN: (数字)9783642394829

ISBN: (纸本)9783642394812

The elderly population in almost every country is growing faster than ever before. However, our knowledge about the aging process is still limited despite decades of studies on this topic. In this report, we focus on the gradual accumulation of DNA damage in cells, which is a key aspect of the aging process and one that underlies age-dependent functional decline in cells, tissues, and organs. To achieve the goal of discriminating DNA-repair from non-DNA-repair genes among currently known genes related to human aging, four machine learning methods were employed: Decision Trees, Naïve Bayes, Support Vector Machine, and Random Forest (RF). Among the four methods, the RF algorithm achieved a total accuracy (ACC) of 97.32% and an area under receiver operating characteristic (AUC) of 0.98. These estimates were based on 18 selected attributes, including 10 Gene Ontology and 8 Protein-Protein Interaction (PPI) attributes. A predictive model built with only 15 PPI attributes achieved performance levels of ACC= 96.56% and AUC=0.95. Systems biology analyses showed that the features of these attributes were related to cancer, genetic, developmental, and neurological disorders, as well as DNA replication/ recombination/repair, cell cycle, cell death, and cell function maintenance. The results of this study indicate that genes indicative of aging may be successfully classified into DNA repair and non-DNA repair genes and such successful classification may help identify pathways and biomarkers that are important to the aging process. © 2013 Springer-Verlag.

关键词： Genes

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ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB 2007)

IEEE Symposium on Computational Intelligence in Bioinformati...

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

The following topics are dealt with: computational intelligence; bioinformatics; and computational biology

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Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae

Cell-cycle phenotyping with conditional random fields: A cas...

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ieee International symposium on Biomedical Imaging

作者： Michael B. Mayhew Alexander J. Hartemink Program in Computational Biology & Bioinformatics Duke University Durham NC USA Department of Computer Science Program in Computational Biology & Bioinformatics Duke University Durham NC USA

ISBN: (纸本)9781467364560

High-resolution, multimodal microscopy grants an intimate view of the inner workings of cells. Complex processes like cell division can be monitored with microscope images, assuming identification of cells and their cell-cycle markers: cellular structures indicative of cell-cycle progress. Here, we explore how spatial relationships between these markers can facilitate their identification. We grew and synchronized Saccharomyces cerevisiae cell cultures and then acquired multimodal image data as the cells proceeded through the cell cycle. We trained a conditional random field model to capture pixel-level spatial relationships among three different cell-cycle markers observable in our images. We observed good predictive performance of this pixel-level model on three held-out test images, and performance improved when we used marker-level information from our training data to prune model predictions. Our results support the use of conditional random fields in bioimage labeling and encourage the use of as much multiscale information as available in training data when identifying cell-cycle markers.

关键词： Labeling Predictive models Biological system modeling Training data Microscopy Data models

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Variations on breast density and subtlety of the findings require different computational intelligence pipelines for the diagnosis of clustered microcalcifications

Variations on breast density and subtlety of the findings re...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Ioannis I. Andreadis George M. Spyrou Panos A. Ligomenides Konstantina S. Nikita School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

In this work, we study the factors that influence the efficacy of a proposed Computer Aided Diagnosis (CAD x ) framework for the diagnosis of clustered microcalcifications (MCs) using a large dataset of mammograms containing cases of varying breast density and findings' subtlety. The reported results indicate that the proposed framework performs towards the right direction, as it appears high classification performance (A z =0.909) for specific subsets of cases, while outperforming at the same time the performance of the radiologists who evaluated the same cases. The effect of the initial enhancement of mammograms in the CAD x pipeline is then investigated, by applying three different image enhancement techniques on several subsets of mammograms. We observed that for the considered subsets of dense mammograms, a wavelet-based enhancement algorithm outperformed the rest and provided superior classification performance (A z =0.849). We indicate therefore that the density of the breast determines the need of different computational algorithms for the analysis of a mammogram and as a result the a priori knowledge of this factor may be exploited for the optimization of the diagnostic process.

关键词： Classification algorithms Breast Image enhancement Pipelines Algorithm design and analysis Clustering algorithms Feature extraction

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BINESA — A software tool for evolution modelling of biochemical networks' structure

BINESA — A software tool for evolution modelling of biochem...

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International symposium on computational intelligence and Informatics

作者： Tatjana Rubina Egils Stalidzans Biosystems group Latvia University of Agriculture Jelgava Latvia

ISBN: (数字)9781479901975

ISBN: (纸本)9781479901944

Biochemical networks of different types such as transcription regulatory, gene regulation, metabolic, protein interaction and signal transduction networks have been extensively studied. One of research directions is related with topological properties of biochemical networks and their structure evolution. Earlier research clearly shows that biochemical networks differ considerably from random ones in their network structure. The reason could be mutation and selection based evolution combined with different importance of particular paths of the network for the survival of an organism. It would be useful to investigate and estimate the changes of network structure that are caused by different types of mutation during the evolution, such as point mutations, missense, nonsense mutations, nucleotide inversion, gene inversion, duplication, deletion, and translocation mutations. BINESA (BIochemical NEtwork Structure Analyser) is a standalone software tool developed for evolution modelling of a biochemical network structure founded on evolutionary changes of an underlying artificial genome. This software tool is written in Visual Basic programming language and developed for Windows operating system using Microsoft Access. BINESA has a database for storing of network structure and artificial genome data. This tool provides the exploration of the evolution dynamics of biochemical network structure, supports SBML and GML models import and export, allows visualization of the network structure in graph form visually marking out reactions of different importance and perform a topological analysis of the biochemical network structure.

关键词： Biological system modeling bioinformatics Genomics Evolution (biology) Software tools Proteins Analytical models

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Enhanced probabilistic latent semantic analysis with weighting schemes to predict genomic annotations

Enhanced probabilistic latent semantic analysis with weighti...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Pietro Pinoli Davide Chicco Marco Masseroli Dipartimento di Elettronica Informazione e Bioingegneria Italy

Genomic annotations with functional controlled terms, such as the Gene Ontology (GO) ones, are paramount in modern biology. Yet, they are known to be incomplete, since the current biological knowledge is far to be definitive. In this scenario, computational methods that are able to support and quicken the curation of these annotations can be very useful. In a previous work, we discussed the benefits of using the Probabilistic Latent Semantic Analysis algorithm in order to predict novel GO annotations, compared to some Singular Value Decomposition (SVD) based approaches. In this paper, we propose a further enhancement of that method, which aims at weighting the available associations between genes and functional terms before using them as input to the predictive system. The tests that we performed on the annotations of human genes to GO functional terms showed the efficacy of our approach.

关键词： Ontologies Semantics bioinformatics Genomics Probabilistic logic Prediction algorithms

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Support Vector-Based Takagi-Sugeno Fuzzy System for the Prediction of Binding Affinity of Peptides

Support Vector-Based Takagi-Sugeno Fuzzy System for the Pred...

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

作者： Volkan Uslan Huseyin Seker Bio-Health Informatics Research Group Centre for Computational Intelligence Faculty of Technology De Montfort University

ISBN: (纸本)9781457702150

High dimensional, complex and non-linear nature of the post-genome data often adversely affects the performance of predictive models. There are two methods that have been widely used to model such non-linear systems, namely Fuzzy System (FS) and Support Vector Machine (SVM). FS is good at modelling uncertainty and yielding a set of interpretable IF-THEN rules, but suffers from the curse of dimensionality whereas SVM is a method that has been shown to effectively deal with large number of dimensions leading to better generalization ability. In this paper, a hybrid system is therefore proposed to improve FS with the aid of SVM-based regression method and successfully applied to the prediction of binding affinity of peptides, which is regarded as one of the most complex modelling problems in the post-genome era due to the diversity of peptides discovered. The proposed hybrid method yields comparatively better results than what has been presented in the recently published papers, therefore can also be considered for other bioinformatics applications.

关键词： Peptides Fuzzy systems Predictive models Support vector machines computational modeling Biological system modeling Robustness

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Modeling of solitary pulmonary nodules in PET/CT images using Monte Carlo methods

Modeling of solitary pulmonary nodules in PET/CT images usin...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： George Tzanoukos Anastasios Gaitanis Alexandros Georgakopoulos Achilleas Chatziioannou Sofia Chatziioannou George Spyrou post-graduate Program: “Information Technologies in Medicine and Biology” (ITMB) National Center for Scientific Research (NCSR) “Demokritos” Athens CO Greece Department of Biomedical Technology Biomedical Research Foundation of the Academy of Athens (BRFAA) Athens Greece Nuclear Medicine Department Biomedical Research Foundation of the Academy of Athens (BRFAA) Athens Greece First Radiology Department University of Athens Athens Greece Biomedical Informatics Unit Biomedical Research Foundation of the Academy of Athens (BRFAA) Athens Greece

The assessment of solitary pulmonary nodules (SPN) is a very difficult task in PET imaging due to adjacent normal structures. Physicians and computational systems as well, would gain benefit if trained to a large number of SPN cases with controlled topological and morphological characteristics. Our objective was to develop a method for the modeling of the solitary pulmonary nodules in CT and PET images. The modeling of SPN was implemented by Monte Carlo methods taking into consideration morphological characteristics, internal features and Standardized Uptake Value (SUV) activity distribution. For the validation of the model, an observer study from three independent medical experts was performed. The reviewers characterized the lesions as simulated or real and finally they classified them as benign or malignant. According to the results of the human observer study a significant percentage of simulated images could not be differentiated from real ones and the simulated class (benign or malignant) was consistent with the observers' classification.

关键词： Positron emission tomography Lesions Computed tomography Cancer Observers computational modeling Monte Carlo methods

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FPSYNT: A FIXED-POINT DATAPATH SYNTHESIS TOOL FOR EMBEDDED SYSTEMS

FPSYNT: A FIXED-POINT DATAPATH SYNTHESIS TOOL FOR EMBEDDED S...

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ieee International Midwest symposium on Circuits and Systems

作者： Ilya Y. Zhbannikov Gregory W. Donohoe Institute of Bioinformatics and Computational Biology (IBEST) University of Idaho Department of Electrical Engineering University of Idaho

ISBN: (纸本)9781479900657

Digital mobile systems must function with low power, small size and weight, and low cost. High-performance desktop microprocessors, with built-in floating point hardware, are not suitable in these cases. For embedded systems, it can be advantageous to implement these calculations with fixed point arithmetic instead. We present an automated fixed-point data path synthesis tool FpSynt for designing embedded applications in fixed-point domain with sufficient accuracy for most applications.

关键词： Embedded systems Computer hardware synthesis Fixed point Floating Fixed-point arithmetic embedded application channels (data transmission)

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