The Experts below are selected from a list of 57873 Experts worldwide ranked by ideXlab platform

Tune Wulff - One of the best experts on this subject based on the ideXlab platform.

  • chomics a web based tool for multi omics data analysis and Interactive Visualization in cho cell lines
    PLOS Computational Biology, 2020
    Co-Authors: Dongdong Lin, Hima Yalamanchili, Xinmin Zhang, Nathan E Lewis, Christina S Alves, Joost Groot, Johnny Arnsdorf, Sara Peterson Bjorn, Tune Wulff
    Abstract:

    Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an Interactive Visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and Interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and Visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, Visualization and management with expectations to promote the broader use of omics in CHO cell research.

  • chomics a web based tool for multi omics data analysis and Interactive Visualization in cho cell lines
    bioRxiv, 2020
    Co-Authors: Dongdong Lin, Hima Yalamanchili, Xinmin Zhang, Nathan E Lewis, Christina S Alves, Joost Groot, Johnny Arnsdorf, Sara Peterson Bjorn, Tune Wulff, Bjorn G Voldborg
    Abstract:

    Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. High-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotype, product quality and to guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an Interactive Visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and Interactively. The tool was also designed with the flexibility to allow other omics data input like proteomics data and thereby enabling multi-omics comparison and Visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, Visualization and management with expectations to promote the broader use of omics in CHO cell research. The open-source tool is freely available at http://www.chomics.org.

Nathan E Lewis - One of the best experts on this subject based on the ideXlab platform.

  • chomics a web based tool for multi omics data analysis and Interactive Visualization in cho cell lines
    PLOS Computational Biology, 2020
    Co-Authors: Dongdong Lin, Hima Yalamanchili, Xinmin Zhang, Nathan E Lewis, Christina S Alves, Joost Groot, Johnny Arnsdorf, Sara Peterson Bjorn, Tune Wulff
    Abstract:

    Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an Interactive Visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and Interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and Visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, Visualization and management with expectations to promote the broader use of omics in CHO cell research.

  • chomics a web based tool for multi omics data analysis and Interactive Visualization in cho cell lines
    bioRxiv, 2020
    Co-Authors: Dongdong Lin, Hima Yalamanchili, Xinmin Zhang, Nathan E Lewis, Christina S Alves, Joost Groot, Johnny Arnsdorf, Sara Peterson Bjorn, Tune Wulff, Bjorn G Voldborg
    Abstract:

    Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. High-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotype, product quality and to guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an Interactive Visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and Interactively. The tool was also designed with the flexibility to allow other omics data input like proteomics data and thereby enabling multi-omics comparison and Visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, Visualization and management with expectations to promote the broader use of omics in CHO cell research. The open-source tool is freely available at http://www.chomics.org.

Jian Chen - One of the best experts on this subject based on the ideXlab platform.

  • enigma viewer Interactive Visualization strategies for conveying effect sizes in meta analysis
    BMC Bioinformatics, 2017
    Co-Authors: Guohao Zhang, Peter Kochunov, Neda Jahanshad, Paul M Thompson, Elliot L Hong, Sinead Kelly, Christopher D Whelan, Jian Chen
    Abstract:

    Global scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using Interactive Visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results. We present ENIGMA-Viewer, an Interactive web-based Visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates Visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies. ENIGMA-Viewer supports presentations and communications of brain research results through effective Visualization designs. By linking Visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website ( http://www.nitrc.org/projects/enigmaviewer_20 ). The tool can also be directly accessed online ( http://enigma-viewer.org ).

  • enigma viewer Interactive Visualization strategies for conveying effect sizes in meta analysis
    International Conference on Bioinformatics, 2016
    Co-Authors: Guohao Zhang, Peter Kochunov, Elliot Hong, Neda Jahanshad, Paul M Thompson, Jian Chen
    Abstract:

    We present ENIGMA-Viewer, an Interactive Visualization tool for scientists to compare effective sizes and association results on standardized regions of interest from different studies, as performed in large scale consortia studies such as those in the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium. We report how Visualization methods are designed for Interactive comparison of brain diffusion tensor imaging (DTI) and cortical thickness statistical test results. ENIGMA-viewer is an open-source software tool, publicly accessible through the NITRC website or online at http://enigma-viewer.org.

Dongdong Lin - One of the best experts on this subject based on the ideXlab platform.

  • chomics a web based tool for multi omics data analysis and Interactive Visualization in cho cell lines
    PLOS Computational Biology, 2020
    Co-Authors: Dongdong Lin, Hima Yalamanchili, Xinmin Zhang, Nathan E Lewis, Christina S Alves, Joost Groot, Johnny Arnsdorf, Sara Peterson Bjorn, Tune Wulff
    Abstract:

    Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an Interactive Visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and Interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and Visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, Visualization and management with expectations to promote the broader use of omics in CHO cell research.

  • chomics a web based tool for multi omics data analysis and Interactive Visualization in cho cell lines
    bioRxiv, 2020
    Co-Authors: Dongdong Lin, Hima Yalamanchili, Xinmin Zhang, Nathan E Lewis, Christina S Alves, Joost Groot, Johnny Arnsdorf, Sara Peterson Bjorn, Tune Wulff, Bjorn G Voldborg
    Abstract:

    Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. High-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotype, product quality and to guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an Interactive Visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and Interactively. The tool was also designed with the flexibility to allow other omics data input like proteomics data and thereby enabling multi-omics comparison and Visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, Visualization and management with expectations to promote the broader use of omics in CHO cell research. The open-source tool is freely available at http://www.chomics.org.

Xin Yan - One of the best experts on this subject based on the ideXlab platform.

  • a work centered visual analytics model to support engineering design with Interactive Visualization and data mining
    Hawaii International Conference on System Sciences, 2012
    Co-Authors: Xin Yan, Timothy W Simpson, Mu Qiao, Gary Stump, Xiaolong Zhang
    Abstract:

    To support the knowledge discovery and decision making from large-scale, multi-dimensional, continuous data sets, novel systems of visual analytics need the capability to identify hidden patterns in data that are critical for in-depth analysis. In this paper, we present a work-centered approach to support visual analytics of complex data sets by combining user-centered Interactive Visualization and data-oriented computational algorithms. We design and implement a specific system prototype, Learning-based Interactive Visualization for Engineering design (LIVE), for engineering designers to handle overwhelming information such as numerous design alternatives generated from automatic simulating software. During the exploration within a "trade space" consisting of possible designs and potential solutions, engineering designers want to analyze the data, discover hidden patterns, and identify preferable solutions. The proposed system allows designers to Interactively examine large design data sets through Visualization and Interactively construct data models from automatic data mining algorithms. We expect that our approach can help designers efficiently and effectively make sense of large-scale design data sets and generate decisions. We also report a preliminary evaluation on our system by analyzing a real engineering design problem related to aircraft wing sizing.

  • live a work centered approach to support visual analytics of multi dimensional engineering design data with Interactive Visualization and data mining
    Design Automation Conference, 2011
    Co-Authors: Xin Yan, Timothy W Simpson, Mu Qiao, Xiaolong Luke Zhang
    Abstract:

    During the process of trade space exploration, information overload has become a notable problem. To find the best design, designers need more efficient tools to analyze the data, explore possible hidden patterns, and identify preferable solutions. When dealing with large-scale, multi-dimensional, continuous data sets (e.g., design alternatives and potential solutions), designers can be easily overwhelmed by the volume and complexity of the data. Traditional information Visualization tools have some limits to support the analysis and knowledge exploration of such data, largely because they usually emphasize the visual presentation of and user interaction with data sets, and lack the capacity to identify hidden data patterns that are critical to in-depth analysis. There is a need for the integration of user-centered Visualization designs and data-oriented data analysis algorithms in support of complex data analysis. In this paper, we present a work-centered approach to support visual analytics of multi-dimensional engineering design data by combining Visualization, user interaction, and computational algorithms. We describe a system, Learning-based Interactive Visualization for Engineering design (LIVE), that allows designer to Interactively examine large design input data and performance output data analysis simultaneously through Visualization. We expect that our approach can help designers analyze complex design data more efficiently and effectively. We report our preliminary evaluation on the use of our system in analyzing a design problem related to aircraft wing sizing.Copyright © 2011 by ASME