ORCID Profile
0000-0003-2242-5440
Current Organisation
University of Nottingham
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Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 09-2011
Publisher: Wiley
Date: 06-2020
DOI: 10.1111/CGF.14035
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: ACM
Date: 07-05-2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 11-02-2011
Publisher: The Royal Society
Date: 12-09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-01-2016
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_78
Publisher: IEEE Comput. Soc
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11618058_42
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: SAGE Publications
Date: 12-10-2016
Abstract: In this article, we introduce a novel timeline visualization technique, TimeSets, that helps make sense of complex temporal datasets by showing the set relationships among in idual events. TimeSets visually groups events that share a topic, such as a place or a person, while preserving their temporal order. It dynamically adjusts the level of detail for each event to suit the amount of information and display estate. Various design options were explored to address issues such as one event belonging to multiple topics. A controlled experiment was conducted to evaluate its effectiveness by comparing it to the KelpFusion method. The results showed significant advantage in accuracy and user preference.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Springer International Publishing
Date: 2014
Publisher: SAGE Publications
Date: 27-06-2013
Abstract: Information officers and network administrators require tools to help them achieve situation awareness about potential network threats. We describe a response to mini-challenge 1 of the 2012 IEEE Visual Analytics Science and Technology challenge in which we developed a visual analytic solution to a network-security situation awareness problem. To support conceptual design, we conducted a series of knowledge elicitation sessions with domain experts. These provided an understanding of the information they needed to make situation awareness judgements as well as a characterisation of those judgements in the form of production rules, which define a parameter we called the ‘concern level assessment’. The concern level assessment was used to provide heuristic guidance within a visual analytic system called Middlesex Spatial Interactive Visualisation Environment. An analysis of Visual Analytics Science and Technology challenge assessment sessions using Middlesex Spatial Interactive Visualisation Environment provides some evidence that intelligent heuristics like these can provide useful guidance without unduly dominating interaction and understanding.
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 07-2009
DOI: 10.1109/IV.2009.55
Publisher: IEEE
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: IEEE
Date: 10-2012
Publisher: American Chemical Society (ACS)
Date: 07-09-2011
DOI: 10.1021/PR200548C
Abstract: Most processes in the cell are delivered by protein complexes, rather than in idual proteins. While the association of proteins has been studied extensively in protein-protein interaction networks (the interactome), an intuitive and effective representation of complex-complex connections (the complexome) is not yet available. Here, we describe a new representation of the complexome of Saccharomyces cerevisiae. Using the core-module-attachment data of Gavin et al. ( Nature 2006 , 440 , 631 - 6 ), protein complexes in the network are represented as nodes these are connected by edges that represent shared core and/or module protein subunits. To validate this network, we examined the network topology and its distribution of biological processes. The complexome network showed scale-free characteristics, with a power law-like node degree distribution and clustering coefficient independent of node degree. Connected complexes in the network showed similarities in biological process that were nonrandom. Furthermore, clusters of interacting complexes reflected a higher-level organization of many cellular functions. The strong functional relationships seen in these clusters, along with literature evidence, allowed 44 uncharacterized complexes to be assigned putative functions using guilt-by-association. We demonstrate our network model using the GEOMI visualization platform, on which we have developed capabilities to integrate and visualize complexome data.
Publisher: Springer Science and Business Media LLC
Date: 26-06-2007
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 02-2007
Publisher: Springer Berlin Heidelberg
Date: 2005
Publisher: IEEE
Date: 02-2007
Publisher: IEEE
Date: 10-2015
Publisher: The Eurographics Association
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
DOI: 10.1109/MCG.2015.50
Publisher: Walter de Gruyter GmbH
Date: 03-2008
DOI: 10.1515/JIB-2008-90
Abstract: Biological data is often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. In this paper, we investigate a new problem of visualising such overlapping biological networks. Two networks overlap if they share some nodes and edges. We present an approach for constructing visualisations of two overlapping networks, based on a restricted three dimensional representation. More specifically, we use three parallel two dimensional planes placed in three dimensions to represent overlapping networks: one for each network (the top and the bottom planes) and one for the overlapping part (in the middle plane). Our method aims to achieve both drawing aesthetics (or conventions) for each in idual network, and highlighting the intersection part by them. Using three biological datasets, we evaluate our visualisation design with the aim to test whether overlapping networks can support the visual analysis of heterogeneous and yet interconnected networks.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2012
Publisher: IEEE
Date: 10-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: MDPI AG
Date: 08-07-2017
DOI: 10.3390/MTI1030013
Publisher: The Royal Society
Date: 15-08-2022
Abstract: We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs—a series of ideas, approaches and methods taken from existing visualization research and practice—deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context open problems upon which the research communities may focus guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See r -vis.github.io/RAMPVIS-PhilTransA-Supplement/ . This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and ex les of overcoming these’.
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 2006
DOI: 10.1109/CGIV.2006.70
Publisher: Springer International Publishing
Date: 2015
Publisher: Wiley
Date: 06-2022
DOI: 10.1111/CGF.14520
Abstract: Epidemiologists use in idual‐based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time‐varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time‐varying infection map while preserving the epidemiological characteristics of each in idual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.
Publisher: IEEE
Date: 07-2008
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 07-2014
DOI: 10.1109/IV.2014.14
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Kai Xu.