VISUAL ANALYTICS OF MULTIMODAL BIOLOGICAL DATA

被引:0
|
作者
Rohn, Hendrik [1 ]
Klukas, Christian [1 ]
Schreiber, Falk [1 ,2 ]
机构
[1] Leibniz Inst Plant Genet & Crop Plant Res IPK, Gatersleben, Germany
[2] Martin Luther Univ Halle Wittenberg, Inst Comp Sci, Halle, Germany
关键词
Visual analytics; Biological data; Integrative visualization; GENOMICS; KEGG; TOOL;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Biological data is measured in increasing quantity and quality, resulting in data describing biological systems from different perspectives. Based on data integration methods, visual data mining and visual analytics can be used to promote the understanding of combined biological data and facilitate the exploration process. In this paper a number of view types are presented and integrated into a comprehensive software tool, in order to support researchers in visualizing flexible combinations of multimodal biological data and to create integrated views on comprehensive datasets spanning multiple "omics" areas. A number of interaction techniques accompany these views, enabling the efficient exploration of the data.
引用
收藏
页码:256 / 261
页数:6
相关论文
共 50 条
  • [41] Artistic data visualization:: Beyond visual analytics
    Viegas, Fernanda B.
    Wattenberg, Martin
    ONLINE COMMUNITIES AND SOCIAL COMPUTING, PROCEEDINGS, 2007, 4564 : 182 - +
  • [42] Visual Context Learning with Big Data Analytics
    Chandrashekar, Mayanka
    Lee, Yugyung
    2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW), 2016, : 600 - 607
  • [43] Agile Visual Analytics in Data Science Systems
    Kandogan, Eser
    Engelke, Ulrich
    PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS), 2016, : 1512 - 1519
  • [44] Automatic Visual Recommendation for Data Science and Analytics
    Muniswamaiah, Manoj
    Agerwala, Tilak
    Tappert, Charles C.
    ADVANCES IN INFORMATION AND COMMUNICATION, VOL 2, 2020, 1130 : 125 - 132
  • [45] Visual Analytics for Big Data using R
    Nasridinov, Aziz
    Park, Young-Ho
    2013 IEEE THIRD INTERNATIONAL CONFERENCE ON CLOUD AND GREEN COMPUTING (CGC 2013), 2013, : 564 - 565
  • [46] An Integrated Visual Analytics Framework for Spatiotemporal Data
    Wang, Shaohua
    Zhong, Ershun
    Zhou, Qiang
    Cui, Xue
    Lu, Hao
    Yun, Weiying
    Hu, Zhongnan
    Cai, Wenwen
    Long, Liang
    PROCEEDINGS OF THE 1ST ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ADVANCES IN RESILIENT AND INTELLIGENT CITIES (ARIC-2018), 2018, : 41 - 45
  • [47] A Reproducibility Study for Visual MRSI Data Analytics
    Jawad, Muhammad
    Molchanov, Vladimir
    Linsen, Lars
    COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2019), 2020, 1182 : 362 - 388
  • [48] PLATO: A visual analytics system for gameplay data
    Wallner, G.
    Kriglstein, S.
    COMPUTERS & GRAPHICS-UK, 2014, 38 : 341 - 356
  • [49] OccVis: a visual analytics system for occultation data
    Cheng, Shiyu
    Shan, Guihua
    Liu, Jun
    Gao, Yang
    Wei, Ping
    Bai, Weihua
    Zhao, Danyang
    JOURNAL OF VISUALIZATION, 2019, 22 (03) : 609 - 624
  • [50] Big Data Visual Analytics with Parallel Coordinates
    Heinrich, Julian
    Broeksema, Bertjan
    2015 BIG DATA VISUAL ANALYTICS (BDVA), 2015,