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  • 1.
    Chavan, Swapnil
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences.
    Abdelaziz, Ahmed
    eADMET GmbH, Germany.
    Wiklander, Jesper G.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences.
    Nicholls, Ian A.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences. Uppsala University.
    A k-nearest neighbor classification of hERG K+ channel blockers2016In: Journal of Computer-Aided Molecular Design, ISSN 0920-654X, E-ISSN 1573-4951, Vol. 30, no 3, p. 229-236Article in journal (Refereed)
    Abstract [en]

    A series of 172 molecular structures that block the hERG K+ channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.

  • 2.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Aleksakhin, Vladyslav
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Schreiber, Falk
    Martin-Luther University.
    Visualization of Mappings between the Gene Ontology and Cluster Trees2012In: Proceedings of the SPIE 2012 Conference on Visualization and Data Analysis (VDA '12) / [ed] Pak Chung Wong, David L. Kao, Ming C. Hao, Chaomei Chen, Robert Kosara, Mark A. Livingston, Jinah Park, and Ian Roberts, SPIE - International Society for Optical Engineering, 2012Conference paper (Refereed)
    Abstract [en]

    Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.

  • 3.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Klukas, Christian
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Schreiber, Falk
    Guiding the Interactive Exploration of Metabolic Pathway Interconnections2012In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 11, no 2, p. 136-150Article in journal (Refereed)
    Abstract [en]

    Approaches to investigate biological processes have been of strong interest in the past few years and are thefocus of several research areas, especially Systems Biology. Biochemical networks as representations ofprocesses are very important for a comprehensive understanding of living beings. Drawings of these networksare often visually overloaded and do not scale. A common solution to deal with this complexity is to divide thecomplete network, for example, the metabolism, into a large set of single pathways that are hierarchicallystructured. If those pathways are visualized, this strategy generates additional navigation and explorationproblems as the user loses the context within the complete network.

    In this article, we present a general solution to this problem of visualizing interconnected pathways anddiscuss it in context of biochemical networks. Our new visualization approach supports the analyst in obtainingan overview to related pathways if they are working within a particular pathway of interest. By usingglyphs, brushing, and topological information of the related pathways, our interactive visualization is ableto intuitively guide the exploration and navigation process, and thus the analysis processes too. To deal withreal data and current networks, our tool has been implemented as a plugin for the VANTED system.

  • 4.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Li, Yuan-Fang
    Monash University, Australia.
    Schreiber, Falk
    University of Konstanz, Germany ; Monash University, Australia.
    BioVis Explorer: A visual guide for biological data visualization techniques2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, article id e0187341Article in journal (Refereed)
    Abstract [en]

    Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers and visualization researchers. To address this challenge, we have reviewed visualization research especially performed for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.

  • 5.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Schreiber, Falk
    Martin Luther University .
    Network Visualization for Integrative Bioinformatics2014In: Approaches in Integrative Bioinformatics: Towards the Virtual Cell / [ed] Ming Chen and Ralf Hofestädt, Berlin Heidelberg: Springer, 2014, p. 173-202Chapter in book (Refereed)
    Abstract [en]

    Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas like systems biology. Biological networks as representations of such processes are crucial for an extensive understanding of living beings. Due to their size and complexity, their growth and continuous change, as well as their compilation from databases on demand, researchers very often request novel network visualization, interaction and exploration techniques. In this chapter, we first provide background information that is needed for the interactive visual analysis of various biological networks. Fields such as (information) visualization, visual analytics and automatic layout of networks are highlighted and illustrated by a number of examples. Then, the state of the art in network visualization for the life sciences is presented together with a discussion of standards for the graphical representation of cellular networks and biological processes.

  • 6.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Schreiber, Falk
    Martin Luther University Halle-Wittenberg.
    Toward the Role of Interaction in Visual Analytics2012In: Proceedings of the 2012 Winter Simulation Conference (WSC '12), 2012, p. 420:1-420:13Conference paper (Refereed)
    Abstract [en]

    This paper firstly provides a general introduction in the most important aspects and ideas of VisualAnalytics. This multidisciplinary field focuses on the analytical reasoning of typically large and complex(often heterogeneous) data sets and combines techniques from interactive visualizations with computationalanalysis methods. Hereby, intuitive and efficient user interactions are a fundamental component which hasto be efficiently supported by any Visual Analytics system. This integration of interaction techniques intoboth visual representations and automatic analysis methods supports the human-information discourse andcan be realized in various ways which is discussed in the second part of the paper. We give examplesof possible applications of Visual Analytics from the domain of biological simulations and highlight theimportance and role of the human in the analysis loop.

  • 7.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Schreiber, Falk
    Monash Univ, Australia.
    Why Integrate InfoVis and SciVis?: An Example from Systems Biology2014In: IEEE Computer Graphics and Applications, ISSN 0272-1716, E-ISSN 1558-1756, Vol. 34, no 6, p. 69-73Article in journal (Other academic)
    Abstract [en]

    The more-or-less artificial barrier between information visualization and scientific visualization hinders knowledge discovery. Having an integrated view of many aspects of the target data, including a seamlessly interwoven visual display of structural abstract data and 3D spatial information, could lead to new discoveries, insights, and scientific questions. Such a view also could reduce the user’s cognitive load—that is, reduce the effort the user expends when comparing views.

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