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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 878 条记录，以下是111-120 订阅

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T-HMM: A Novel Biomedical Text Classifier Based on Hidden Markov Models 8

T-HMM: A Novel Biomedical Text Classifier Based on Hidden Ma...

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8th International conference on Practical Applications of computational Biology & bioinformatics (PACBB)

作者： Seara Vieira, A. Iglesias, E. L. Borrajo, L. Univ Vigo Escola Super Enxeneria Informat Dept Comp Sci Orense Spain

ISBN: (纸本)9783319075815;9783319075808

In this paper, we propose an original model for the classification of biomedical texts stored in large document corpora. The model classifies scientific documents according to their content using information retrieval techniques and Hidden Markov Models. To demonstrate the efficiency of the model, we present a set of experiments which have been performed on OHSUMED biomedical corpus, a subset of the MEDLINE database, and the Allele and GO TREC corpora. Our classifier is also compared with Naive Bayes, k-NN and SVM techniques. Experiments illustrate the effectiveness of the proposed approach. Results show that the model is comparable to the SVM technique in the classification of biomedical texts.

关键词： Hidden Markov Model Text classification bioinformatics

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Unifying the Integration, Analysis and Interpretation of multi-omic datasets: Exploration of the disease networks of Obstructive Nephropathy in children

Unifying the Integration, Analysis and Interpretation of mul...

引用

33rd Annual International conference of the ieee Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Moulos, Panagiotis Valavanis, Ioannis Klein, Julie Maglogiannis, Ilias Schanstra, Joost Chatziioannou, Aristotelis Natl Hellen Res Fdn Inst Biol Res & Biotechnol Athens Greece

ISBN: (纸本)9781424441228

The wealth of data amassed by the utilization of various high-throughput techniques, in various layers of molecular dissection, stresses the critical role of the unification of the computational methodologies applied in biological data handling, storage, analysis and visualization. In this article, a generic workflow is showcased in a multi-omic dataset that is used to study Obstructive Nephropathy (ON) in children, integrating microarray data from several biological layers (transcriptomic, post-transcriptomic, proteomic). The workflow exploits raw measurements and through several analytical stages (preprocessing, statistical and functional), which entail various parsing steps, reaches the visualization stage of the heterogeneous, broader, molecular interacting network derived. This network, where the interconnected entities are exploiting the knowledge stored in public repositories, represents a systems level interpretation of the pathological state probed.

关键词： bioinformatics Data visualization Genomics Mice Proteins Proteomics bioinformatics biological data analysis biological data handling biological data storage biological data visualization biological techniques biomedical engineering complex networks comput

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A BCHC genetic algorithm model of cotemporal hierarchical Arabidopsis thaliana gene interactions

A BCHC genetic algorithm model of cotemporal hierarchical <i...

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ieee International conference on bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： LaPointe, Bree Ann John, David J. Norris, James L. Allen, Edward E. Harkey, Alexandria F. Muhlemann, Joelle K. Muday, Gloria Qualtrix Salt Lake City UT USA Wake Forest Univ Dept Comp Sci Winston Salem NC 27109 USA Wake Forest Univ Winston Salem NC 27109 USA

ISBN: (纸本)9781538654880

Gene interaction network models from time course gene transcript abundance data are algorithmically created using a new aggressive genetic algorithm denoted by BCHC. The BCHC algorithm rigorously integrates probabilistic hierarchical likelihood and Bayesian methodology to produce accurate posterior probabilities of interactions between genes after observance of hierarchical gene transcript abundance data. Forbidden pairwise gene relationships are incorporated into the modeling process. This gene interaction model is compared to a previous gene interaction model utilizing the same data and Bayesian likelihood, however based upon an exponentially slower, less aggressive, and less adaptive Metropolis-Hasting search algorithm. In addition for a smaller data set, our gene interaction model is compared to less rigorous non-probabilistic Lasso estimated partial correlation models which do not fully incorporate the hierarchical structure. A comparison is also made between the smallest Bayesian model and tests for edges based on a restricted non-Bayesian hierarchical technique. The BCHC algorithm performs well when the number of genes is moderately increased, both in terms of execution time and model quality.

关键词： bioinformatics Biological system modeling computational systems biology Genetic algorithms Probability Bayes methods

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A multi-tenant computational platform for translational medicine 38

A multi-tenant computational platform for translational medi...

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38th ieee International conference on Distributed Computing systems (ICDCS)

作者： Oehmichen, Axel Guitton, Florian Agapow, Paul Emam, Ibrahim Guo, Yike Imperial Coll London Data Sci Inst London SW7 2AZ England

ISBN: (纸本)9781538668719

Translational biomedical research has become a science driven by big data. Improving patient care by developing personalized therapies and new drugs depends increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of largescale internal and external, partner and public, data sources. As analysing these large-scale and complex datasets has become increasingly computationally expensive, it is of paramount importance to enable researchers to seamlessly scale up their computation platform while being able to manage complex yet flexible scenario that biomedical scientists are asking for. We developed a new platform as an answer to those needs of analysing and exploring massive amounts of medical data with the constrain of enabling the broadest audience, ranging from the medical doctor to the advanced coders, to easily and intuitively exploit this new resource. The platform consists of three main components: Borderline UI, the eTRIKS Analytical Environment (eAE)(1) [I] and the eTRIKS Data Platform (eDP). Each component has been designed to operate on specific sets of problems statelessly, developed as independent open-source software packages, and connected loosely in a cloud-native fashion [2] to form a coherent platform for large scale medical data analysis. The code is fully open source and available on GitHub at this address: https://***/dsi-icl

关键词： Drugs Computer architecture Big Data Biomedical imaging bioinformatics Respiratory system Data analysis

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Big Data Analytics in bioinformatics: Transforming Genomics and Proteomics for Advanced Biological Insights

Big Data Analytics in Bioinformatics: Transforming Genomics ...

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2024 International conference on Big Data Analytics in bioinformatics, DABCon 2024

作者： Vohra, D.S. Roorkee Geomatics Division RoorkeePC-247667 India

ISBN: (纸本)9798350378047

The integration of big data analytics into bioinformatics has revolutionized biological research and healthcare. This paper explores the methodologies, tools, and big data analytics use cases in bioinformatics, focusing on genomics, proteomics, and systems biology. Key methodologies include data collection and storage, preprocessing, and advanced data driven approaches like machine learning, statistical analysis, and visual analytics. Applications in genomics involve association studies across the genome and identification of gene diseases, while proteomics benefits from mass spectrometry data analysis. systems biology leverages integrated data for comprehensive models of biological net/orks. Challenges such as data integration, computational complexity, and ethical concerns are discussed, alongside future directions involving advanced algorithms, improved data integration, and secure data-sharing technologies. This interdisciplinary domain holds immense potential for advancing science and medicine. © 2024 ieee.

关键词： Genome

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HPC+Azure environment for bioinformatics applications

HPC+Azure environment for bioinformatics applications

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2013 ieee International conference on bioinformatics and Biomedicine, ieee BIBM 2013

作者： Sidhu, Amandeep S. Balakrishnan, Suresh Reuben Dhillon, Sarinder K. School of Engineering and Science Curtin University Sarawak Malaysia Faculty of Health Sciences Curtin University Perth WA Australia Faculty of Science University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9781479913091

In the past 20 years, huge flow of data, produced by the nonstop rise of computational power has led to a paradigm shift in large scale data processing mechanisms and computing architecture. As a result, human and computational resources are needed to aid data-intensive operations which will cause the high degree of storage and management expenses. An organized and standard approach is important to manage these issues with an architecture that able to scale into the predictable future. Instead of the fastest and largest single computer solution, economical clusters of computers can better manage and process all data. Most of the high-performance computing (HPC) systems need a huge amount of processing power and Windows Azure is capable of providing a huge quantity of processing power on demand. As the Windows HPC server and Windows Azure combine, the cloud and on-premises world are now able to function together. In this paper we explore a HPC+Azure implementation model and demonstrate by running a genome sequence assembly application. © 2013 ieee.

关键词： Digital storage

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Automatic prediction of functional site regions in low-resolution protein structures

Automatic prediction of functional site regions in low-resol...

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Proceedings - 2004 ieee computational systems bioinformatics conference, CSB 2004

作者： Sodhi, Jaspreet Singh McGuffin, Liam J. Bryson, Kevin Ward, Jonathan J. Wernisch, Lorenz Jones, David T. Bioinformatics Group Department of Computer Science University College London London United Kingdom

ISBN: (纸本)0769521940

World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.

关键词： Proteins

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Intuitionistic Fuzziness and Modality 10

Intuitionistic Fuzziness and Modality

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10th ieee International conference on Intelligent systems, IS 2020

作者： Atanassov, Krassimir Dept. of Bioinformatics and Mathematical Modelling Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Bl. 105 Acad. G. Bonchev Str. Sofia1113 Bulgaria Intelligent Systems Laboratory Prof. Asen Zlatarov University Burgas8010 Bulgaria

ISBN: (纸本)9781728154565

Ever since the first publication over intuitionistic fuzzinsess, the two standard modal operators "necessity"( ? ) and "possibility"(?) are introduced over intuitionistic fuzzy sets. In the years, these two operators are object of a lot of extensions and modifications. In the present paper, a new direction of tow modifications of the two standard modal operators is discussed. We introduce four new operators and study their properties. © 2020 ieee.

关键词： Fuzzy sets

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9th International conference on Practical Applications of computational Biology and bioinformatics 9

9th International Conference on Practical Applications of Co...

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9th International conference on Practical Applications of computational Biology and bioinformatics, PACBB 2015

作者： Overbeek, Ross Rocha, Miguel P. Fdez-Riverola, Florentino De Paz, Juan F. Fellowship for the Interpretation of Genomes Burr RidgeIL United States Department of Informatics Centre of Biological Engineering University of Minho Braga Portugal Department of Informatics ESEI: Escuela Superior de Ingeniería Informática University of Vigo Ourense Spain Departamento de Informática y Automática University of Salamanca Salamanca Spain

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Signature Discovery for Personalized Medicine

Signature Discovery for Personalized Medicine

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11th ieee International conference on Intelligence and Security Informatics (ieee ISI)

作者： Yeung, Ka Yee Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781467362139;9781467362146

Various types of genome-wide data, such as sequence and gene expression data, have been generated and are available from public databases. These genome-wide data present major computational challenges as the number of variables far exceeds the number of observations. Many computational tools have been developed for the analyses of these high dimensional data, and these methods have led to improved understanding of molecular biology. In particular, signature discovery (also known as variable selection or feature selection), a machine learning technique in which subsets of variables are selected to build robust models, are useful in mining these highdimensional functional genomic data. In this paper, we will review the applications of signature discovery methods in mining these high dimensional data. Specifically, we will focus on two applications, namely, the identification of signature genes predictive of disease phenotypes and the inference of regulatory networks. Signature genes predictive of disease phenotypes can be potentially used in the diagnosis and prognosis of diseases. Regulatory networks that capture the gene-to-gene influences can be used to provide the context of therapeutic intervention.

关键词： bioinformatics Machine learning Regression analysis systems biology

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