The Experts below are selected from a list of 545139 Experts worldwide ranked by ideXlab platform
Habtom W. Ressom - One of the best experts on this subject based on the ideXlab platform.
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SIMAT: GC-SIM-MS Data Analysis Tool
BMC Bioinformatics, 2015Co-Authors: Mohammad R. Nezami Ranjbar, Cristina Di Poto, Yue Wang, Habtom W. RessomAbstract:Background Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative Analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted Analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software Tools specifically designed for Analysis of GC-SIM-MS Data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative Analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The Tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS Data can be imported in netCDF or NIST mass spectral library (MSL) formats. Results We evaluated the performance of SIMAT using two GC-SIM-MS Datasets obtained by targeted Analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by Analysis of GC-SIM-MS Data. Conclusions We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based Analysis. Also, various functions and algorithms are implemented in the Tool to: (1) read and import raw Data and spectral libraries; (2) perform GC-SIM-MS Data preprocessing; and (3) plot and visualize EICs and TICs.
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SIMAT: GC-SIM-MS Data Analysis Tool
BMC bioinformatics, 2015Co-Authors: Mohammad Ranjbar, Cristina Di Poto, Yue Joseph Wang, Habtom W. RessomAbstract:Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative Analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted Analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software Tools specifically designed for Analysis of GC-SIM-MS Data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative Analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The Tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS Data can be imported in netCDF or NIST mass spectral library (MSL) formats. We evaluated the performance of SIMAT using two GC-SIM-MS Datasets obtained by targeted Analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by Analysis of GC-SIM-MS Data. We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based Analysis. Also, various functions and algorithms are implemented in the Tool to: (1) read and import raw Data and spectral libraries; (2) perform GC-SIM-MS Data preprocessing; and (3) plot and visualize EICs and TICs.
Cristina Di Poto - One of the best experts on this subject based on the ideXlab platform.
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SIMAT: GC-SIM-MS Data Analysis Tool
BMC Bioinformatics, 2015Co-Authors: Mohammad R. Nezami Ranjbar, Cristina Di Poto, Yue Wang, Habtom W. RessomAbstract:Background Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative Analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted Analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software Tools specifically designed for Analysis of GC-SIM-MS Data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative Analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The Tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS Data can be imported in netCDF or NIST mass spectral library (MSL) formats. Results We evaluated the performance of SIMAT using two GC-SIM-MS Datasets obtained by targeted Analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by Analysis of GC-SIM-MS Data. Conclusions We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based Analysis. Also, various functions and algorithms are implemented in the Tool to: (1) read and import raw Data and spectral libraries; (2) perform GC-SIM-MS Data preprocessing; and (3) plot and visualize EICs and TICs.
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SIMAT: GC-SIM-MS Data Analysis Tool
BMC bioinformatics, 2015Co-Authors: Mohammad Ranjbar, Cristina Di Poto, Yue Joseph Wang, Habtom W. RessomAbstract:Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative Analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted Analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software Tools specifically designed for Analysis of GC-SIM-MS Data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative Analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The Tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS Data can be imported in netCDF or NIST mass spectral library (MSL) formats. We evaluated the performance of SIMAT using two GC-SIM-MS Datasets obtained by targeted Analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by Analysis of GC-SIM-MS Data. We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based Analysis. Also, various functions and algorithms are implemented in the Tool to: (1) read and import raw Data and spectral libraries; (2) perform GC-SIM-MS Data preprocessing; and (3) plot and visualize EICs and TICs.
Lihan Huang - One of the best experts on this subject based on the ideXlab platform.
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ipmp global fit a one step direct Data Analysis Tool for predictive microbiology
International Journal of Food Microbiology, 2017Co-Authors: Lihan HuangAbstract:Abstract The objective of this work is to develop and validate a unified optimization algorithm for performing one-step global regression Analysis of isothermal growth and survival curves for determination of kinetic parameters in predictive microbiology. The algorithm is incorporated with user-friendly graphical interfaces (GUIs) to develop a Data Analysis Tool, the USDA IPMP-Global Fit. The GUIs are designed to guide the users to easily navigate through the Data Analysis process and properly select the initial parameters for different combinations of mathematical models. The software is developed for one-step kinetic Analysis to directly construct tertiary models by minimizing the global error between the experimental observations and mathematical models. The current version of the software is specifically designed for constructing tertiary models with time and temperature as the independent model parameters in the package. The software is tested with a total of 9 different combinations of primary and secondary models for growth and survival of various microorganisms. The results of Data Analysis show that this software provides accurate estimates of kinetic parameters. In addition, it can be used to improve the experimental design and Data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology.
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ipmp 2013 a comprehensive Data Analysis Tool for predictive microbiology
International Journal of Food Microbiology, 2014Co-Authors: Lihan HuangAbstract:Abstract Predictive microbiology is an area of applied research in food science that uses mathematical models to predict the changes in the population of pathogenic or spoilage microorganisms in foods exposed to complex environmental changes during processing, transportation, distribution, and storage. It finds applications in shelf-life prediction and risk assessments of foods. The objective of this research was to describe the performance of a new user-friendly comprehensive Data Analysis Tool, the Integrated Pathogen Modeling Model (IPMP 2013), recently developed by the USDA Agricultural Research Service. This Tool allows users, without detailed programming knowledge, to analyze experimental kinetic Data and fit the Data to known mathematical models commonly used in predictive microbiology. Data curves previously published in literature were used to test the models in IPMP 2013. The accuracies of the Data Analysis and models derived from IPMP 2013 were compared in parallel to commercial or open-source statistical packages, such as SAS® or R. Several models were analyzed and compared, including a three-parameter logistic model for growth curves without lag phases, reduced Huang and Baranyi models for growth curves without stationary phases, growth models for complete growth curves (Huang, Baranyi, and re-parameterized Gompertz models), survival models (linear, re-parameterized Gompertz, and Weibull models), and secondary models (Ratkowsky square-root, Huang square-root, Cardinal, and Arrhenius-type models). The comparative Analysis suggests that the results from IPMP 2013 were equivalent to those obtained from SAS® or R. This work suggested that the IPMP 2013 could be used as a free alternative to SAS®, R, or other more sophisticated statistical packages for model development in predictive microbiology.
Robert Levinson - One of the best experts on this subject based on the ideXlab platform.
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Research Environment for Data Analysis Tool Allocators
Applied Intelligence, 1999Co-Authors: Jeff Wilkinson, Robert LevinsonAbstract:Intelligent Data Analysis implies the reasoned application of autonomous or semi-autonomous Tools to Data sets drawn from problem domains. Automation of this process of reasoning about Analysis (based on factors such as available computational resources, cost of Analysis, risk of failure, lessons learned from past errors, and tentative structural models of problem domains) is highly non-trivial. By casting the problem of reasoning about Analysis (MetaReasoning) as yet another Data Analysis problem domain, we have previously [R. Levinson and J. Wilkinson, in Advances in Intelligent Data Analysis, edited by X. Liu, P. Cohen, and M. Berthold, volume LNCS 1280, Springer-Verlag, Berlin, pp. 89–100, 1997] presented a design framework, MetaReasoning for Data Analysis Tool Allocation (MRData). Crucial to this framework is the ability of a Tool Allocator to track resource consumption (i.e. processor time and memory usage) by the Tools it employs, as well as the ability to allocate measured quantities of resources to these Tools. In order to test implementations of the MRData design, we now implement a Runtime Environment for Data Analysis Tool Allocation, RE:Data. Tool Allocators run as processes under RE:Data, are allotted system resources, and may use these resources to run their Tools as spawned sub-processes. We also present designs of native RE:Data implementations of Analysis Tools used by MRData: K-Nearest Neighbor Tables, Regression Trees, Interruptible (“Any-Time”) Regression Trees, and “Hierarchy Diffusion” Temporal Difference Learners. Preliminary results are discussed and techniques for integration with non-native Tools are explored.
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IDA - Meta-Reasoning for Data Analysis Tool Allocation
Advances in Intelligent Data Analysis Reasoning about Data, 1997Co-Authors: Robert Levinson, Jeff WilkinsonAbstract:It is desirable that Data Analysis Tools become more autonomous in managing their computational resources, minimizing risk and cost, assessing their errors, developing new representations, and integrating with other Data Analysis Tools. To aid in this development we introduce a design for using Meta-Reasoning in a Data Analysis Tool Allocation system that employs analogical and structural reasoning to learn from and across domains and its experience. We give a formal definition of a “Data Analysis Game” that allows the implementation of a hierarchical learning framework suitable for describing and exploring real-world Analysis problems. The framework may also be used to analyze the performance of the Tool Allocation system itself, allowing it to self-optimize. If the integration of Tools is performed correctly, it should allow a cost-efficient level of performance not obtainable with a single Tool alone or with unsystematic use of a group of Tools.
Mohammad Ranjbar - One of the best experts on this subject based on the ideXlab platform.
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SIMAT: GC-SIM-MS Data Analysis Tool
BMC bioinformatics, 2015Co-Authors: Mohammad Ranjbar, Cristina Di Poto, Yue Joseph Wang, Habtom W. RessomAbstract:Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative Analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted Analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software Tools specifically designed for Analysis of GC-SIM-MS Data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative Analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The Tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS Data can be imported in netCDF or NIST mass spectral library (MSL) formats. We evaluated the performance of SIMAT using two GC-SIM-MS Datasets obtained by targeted Analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by Analysis of GC-SIM-MS Data. We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based Analysis. Also, various functions and algorithms are implemented in the Tool to: (1) read and import raw Data and spectral libraries; (2) perform GC-SIM-MS Data preprocessing; and (3) plot and visualize EICs and TICs.
