The Experts below are selected from a list of 279837 Experts worldwide ranked by ideXlab platform

Chengjian Zheng - One of the best experts on this subject based on the ideXlab platform.

  • chemical fingerprint and quantitative Analysis for the quality evaluation of vitex negundo seeds by reversed phase high performance liquid chromatography coupled with hierarchical Clustering Analysis
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Xiuqing Li, Khalid Rahman, Chengjian Zheng
    Abstract:

    A simple and efficient method was developed for the chemical fingerprint Analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint Analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical Clustering Analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds.

  • chemical fingerprint and quantitative Analysis for the quality evaluation of vitex negundo seeds by reversed phase high performance liquid chromatography coupled with hierarchical Clustering Analysis
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Xiuqing Li, Khalid Rahman, Chengjian Zheng
    Abstract:

    A simple and efficient method was developed for the chemical fingerprint Analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint Analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical Clustering Analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds. This article is protected by copyright. All rights reserved.

Xiuqing Li - One of the best experts on this subject based on the ideXlab platform.

  • chemical fingerprint and quantitative Analysis for the quality evaluation of vitex negundo seeds by reversed phase high performance liquid chromatography coupled with hierarchical Clustering Analysis
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Xiuqing Li, Khalid Rahman, Chengjian Zheng
    Abstract:

    A simple and efficient method was developed for the chemical fingerprint Analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint Analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical Clustering Analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds.

  • chemical fingerprint and quantitative Analysis for the quality evaluation of vitex negundo seeds by reversed phase high performance liquid chromatography coupled with hierarchical Clustering Analysis
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Xiuqing Li, Khalid Rahman, Chengjian Zheng
    Abstract:

    A simple and efficient method was developed for the chemical fingerprint Analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint Analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical Clustering Analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds. This article is protected by copyright. All rights reserved.

Khalid Rahman - One of the best experts on this subject based on the ideXlab platform.

  • chemical fingerprint and quantitative Analysis for the quality evaluation of vitex negundo seeds by reversed phase high performance liquid chromatography coupled with hierarchical Clustering Analysis
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Xiuqing Li, Khalid Rahman, Chengjian Zheng
    Abstract:

    A simple and efficient method was developed for the chemical fingerprint Analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint Analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical Clustering Analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds.

  • chemical fingerprint and quantitative Analysis for the quality evaluation of vitex negundo seeds by reversed phase high performance liquid chromatography coupled with hierarchical Clustering Analysis
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Xiuqing Li, Khalid Rahman, Chengjian Zheng
    Abstract:

    A simple and efficient method was developed for the chemical fingerprint Analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint Analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical Clustering Analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds. This article is protected by copyright. All rights reserved.

Cagatay Demiralp - One of the best experts on this subject based on the ideXlab platform.

  • clustrophile 2 guided visual Clustering Analysis
    IEEE Transactions on Visualization and Computer Graphics, 2019
    Co-Authors: Marco Cavallo, Cagatay Demiralp
    Abstract:

    Data Clustering is a common unsupervised learning method frequently used in exploratory data Analysis. However, identifying relevant structures in unlabeled, high-dimensional data is nontrivial, requiring iterative experimentation with Clustering parameters as well as data features and instances. The number of possible Clusterings for a typical dataset is vast, and navigating in this vast space is also challenging. The absence of ground-truth labels makes it impossible to define an optimal solution, thus requiring user judgment to establish what can be considered a satisfiable Clustering result. Data scientists need adequate interactive tools to effectively explore and navigate the large Clustering space so as to improve the effectiveness of exploratory Clustering Analysis. We introduce Clustrophile 2 , a new interactive tool for guided Clustering Analysis. Clustrophile 2 guides users in Clustering-based exploratory Analysis, adapts user feedback to improve user guidance, facilitates the interpretation of clusters, and helps quickly reason about differences between Clusterings. To this end, Clustrophile 2 contributes a novel feature, the Clustering Tour, to help users choose Clustering parameters and assess the quality of different Clustering results in relation to current Analysis goals and user expectations. We evaluate Clustrophile 2 through a user study with 12 data scientists, who used our tool to explore and interpret sub-cohorts in a dataset of Parkinson's disease patients. Results suggest that Clustrophile 2 improves the speed and effectiveness of exploratory Clustering Analysis for both experts and non-experts.

  • clustrophile 2 guided visual Clustering Analysis
    arXiv: Human-Computer Interaction, 2018
    Co-Authors: Marco Cavallo, Cagatay Demiralp
    Abstract:

    Data Clustering is a common unsupervised learning method frequently used in exploratory data Analysis. However, identifying relevant structures in unlabeled, high-dimensional data is nontrivial, requiring iterative experimentation with Clustering parameters as well as data features and instances. The number of possible Clusterings for a typical dataset is vast, and navigating in this vast space is also challenging. The absence of ground-truth labels makes it impossible to define an optimal solution, thus requiring user judgment to establish what can be considered a satisfiable Clustering result. Data scientists need adequate interactive tools to effectively explore and navigate the large Clustering space so as to improve the effectiveness of exploratory Clustering Analysis. We introduce \textit{Clustrophile~2}, a new interactive tool for guided Clustering Analysis. \textit{Clustrophile~2} guides users in Clustering-based exploratory Analysis, adapts user feedback to improve user guidance, facilitates the interpretation of clusters, and helps quickly reason about differences between Clusterings. To this end, \textit{Clustrophile~2} contributes a novel feature, the Clustering Tour, to help users choose Clustering parameters and assess the quality of different Clustering results in relation to current Analysis goals and user expectations. We evaluate \textit{Clustrophile~2} through a user study with 12 data scientists, who used our tool to explore and interpret sub-cohorts in a dataset of Parkinson's disease patients. Results suggest that \textit{Clustrophile~2} improves the speed and effectiveness of exploratory Clustering Analysis for both experts and non-experts.

Marco Cavallo - One of the best experts on this subject based on the ideXlab platform.

  • clustrophile 2 guided visual Clustering Analysis
    IEEE Transactions on Visualization and Computer Graphics, 2019
    Co-Authors: Marco Cavallo, Cagatay Demiralp
    Abstract:

    Data Clustering is a common unsupervised learning method frequently used in exploratory data Analysis. However, identifying relevant structures in unlabeled, high-dimensional data is nontrivial, requiring iterative experimentation with Clustering parameters as well as data features and instances. The number of possible Clusterings for a typical dataset is vast, and navigating in this vast space is also challenging. The absence of ground-truth labels makes it impossible to define an optimal solution, thus requiring user judgment to establish what can be considered a satisfiable Clustering result. Data scientists need adequate interactive tools to effectively explore and navigate the large Clustering space so as to improve the effectiveness of exploratory Clustering Analysis. We introduce Clustrophile 2 , a new interactive tool for guided Clustering Analysis. Clustrophile 2 guides users in Clustering-based exploratory Analysis, adapts user feedback to improve user guidance, facilitates the interpretation of clusters, and helps quickly reason about differences between Clusterings. To this end, Clustrophile 2 contributes a novel feature, the Clustering Tour, to help users choose Clustering parameters and assess the quality of different Clustering results in relation to current Analysis goals and user expectations. We evaluate Clustrophile 2 through a user study with 12 data scientists, who used our tool to explore and interpret sub-cohorts in a dataset of Parkinson's disease patients. Results suggest that Clustrophile 2 improves the speed and effectiveness of exploratory Clustering Analysis for both experts and non-experts.

  • clustrophile 2 guided visual Clustering Analysis
    arXiv: Human-Computer Interaction, 2018
    Co-Authors: Marco Cavallo, Cagatay Demiralp
    Abstract:

    Data Clustering is a common unsupervised learning method frequently used in exploratory data Analysis. However, identifying relevant structures in unlabeled, high-dimensional data is nontrivial, requiring iterative experimentation with Clustering parameters as well as data features and instances. The number of possible Clusterings for a typical dataset is vast, and navigating in this vast space is also challenging. The absence of ground-truth labels makes it impossible to define an optimal solution, thus requiring user judgment to establish what can be considered a satisfiable Clustering result. Data scientists need adequate interactive tools to effectively explore and navigate the large Clustering space so as to improve the effectiveness of exploratory Clustering Analysis. We introduce \textit{Clustrophile~2}, a new interactive tool for guided Clustering Analysis. \textit{Clustrophile~2} guides users in Clustering-based exploratory Analysis, adapts user feedback to improve user guidance, facilitates the interpretation of clusters, and helps quickly reason about differences between Clusterings. To this end, \textit{Clustrophile~2} contributes a novel feature, the Clustering Tour, to help users choose Clustering parameters and assess the quality of different Clustering results in relation to current Analysis goals and user expectations. We evaluate \textit{Clustrophile~2} through a user study with 12 data scientists, who used our tool to explore and interpret sub-cohorts in a dataset of Parkinson's disease patients. Results suggest that \textit{Clustrophile~2} improves the speed and effectiveness of exploratory Clustering Analysis for both experts and non-experts.