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| 2. |
- Hamon, Thierry, et al.
(författare)
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Combining Compositionality and Pagerank for the Identification of Semantic Relations between Biomedical Words
- 2012
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Ingår i: BioNLP. - 9781937284206 - 1937284204 ; , s. 109-117
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Konferensbidrag (refereegranskat)abstract
- The acquisition of semantic resources and relations is an important task for several applications, such as query expansion, information retrieval and extraction, machine translation. However, their validity should also be computed and indicated, especially for automatic systems and applications. We exploit the compositionality based methods for the acquisition of synonymy relations and of indicators of these synonyms. We then apply pagerank-derived algorithm to the obtained semantic graph in order to filter out the acquired synonyms. Evaluation performed with two independent experts indicates that the quality of synonyms is systematically improved by 10 to 15% after their filtering.
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| 3. |
- Bååth, Rasmus, et al.
(författare)
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A prototype based resonance model of rhythm categorization
- 2014
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Ingår i: i-Perception. - : SAGE Publications. - 2041-6695. ; 5:6, s. 548-558
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Tidskriftsartikel (refereegranskat)abstract
- Categorization of rhythmic patterns is prevalent in musical practice, an example of this being the transcription of (possibly not strictly metrical) music into musical notation. In this article we implement a dynamical systems’ model of rhythm categorization based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model accurately replicates the experimental data. Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.
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| 4. |
- Svensson, Lennart
(författare)
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Image analysis and interactive visualization techniques for electron microscopy tomograms
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Images are an important data source in modern science and engineering. A continued challenge is to perform measurements on and extract useful information from the image data, i.e., to perform image analysis. Additionally, the image analysis results need to be visualized for best comprehension and to enable correct assessments. In this thesis, research is presented about digital image analysis and three-dimensional (3-D) visualization techniques for use with transmission electron microscopy (TEM) image data and in particular electron tomography, which provides 3-D reconstructions of the nano-structures. The electron tomograms are difficult to interpret because of, e.g., low signal-to-noise ratio, artefacts that stem from sample preparation and insufficient reconstruction information. Analysis is often performed by visual inspection or by registration, i.e., fitting, of molecular models to the image data. Setting up a visualization can however be tedious, and there may be large intra- and inter-user variation in how visualization parameters are set. Therefore, one topic studied in this thesis concerns automatic setup of the transfer function used in direct volume rendering of these tomograms. Results indicate that histogram and gradient based measures are useful in producing automatic and coherent visualizations. Furthermore, research has been conducted concerning registration of templates built using molecular models. Explorative visualization techniques are presented that can provide means of visualizing and navigating model parameter spaces. This gives a new type of visualization feedback to the biologist interpretating the TEM data. The introduced probabilistic template has an improved coverage of the molecular flexibility, by incorporating several conformations into a static model. Evaluation by cross-validation shows that the probabilistic template gives a higher correlation response than using a Protein Databank (PDB) devised model. The software ProViz (for Protein Visualization) is also introduced, where selected developed techniques have been incorporated and are demonstrated in practice.
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| 5. |
- Kerren, Andreas, 1971-, et al.
(författare)
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Network Visualization for Integrative Bioinformatics
- 2014
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Ingår i: Approaches in Integrative Bioinformatics. - Berlin Heidelberg : Springer. - 9783642412806 - 9783642412813 ; , s. 173-202
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Bokkapitel (refereegranskat)abstract
- 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.
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| 6. |
- Kerren, Andreas, 1971-, et al.
(författare)
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Why Integrate InfoVis and SciVis? : An Example from Systems Biology
- 2014
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Ingår i: IEEE Computer Graphics and Applications. - : IEEE. - 0272-1716 .- 1558-1756. ; 34:6, s. 69-73
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- 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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| 7. |
- Liakata, Maria, et al.
(författare)
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Automatic recognition of conceptualisation zones in scientific articles and two life science applications
- 2012
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1460-2059 .- 1367-4811 .- 1367-4803. ; 28:7, s. 991-1000
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of eleven categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within a paper and their automatic recognition can greatly facilitate biomedical information extraction by characterising the different types of facts, hypotheses and evidence available in a scientific publication. Results: We have trained and compared machine learning classifiers (SVM and CRF) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognise CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with `Experiment', `Background' and `Model' being the categories with the highest F1-scores (76%, 62% and 53% respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We also discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. Availability: A web-based tool for the automatic annotation of papers with CoreSCs and corresponding documentation is available on-line at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated papers, which served as our training data. Contact: liakata@ebi.ac.uk
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| 8. |
- Linde, Oskar, 1979-, et al.
(författare)
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Composed Complex-Cue Histograms : An Investigation of the Information Content in Receptive Field Based Image Descriptors for Object Recognition
- 2012
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Ingår i: Computer Vision and Image Understanding. - : Elsevier. - 1077-3142 .- 1090-235X. ; 116:4, s. 538-560
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Tidskriftsartikel (refereegranskat)abstract
- Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations. This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories. It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complex-cue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive experimental evaluations on two mutually independent data sets. For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatio-chromatic) differential invariants up to order two lead to very high recognition rates. For the task of category classification, primary information is contained in both first- and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue. Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.
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| 9. |
- Mathew, C, et al.
(författare)
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Data Refinement Workflow
- 2012
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Ingår i: http://www.myexperiment.org/packs/267.html.
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The Taxonomic Data Refinement Workflow provides an environment for preparing observational and specimen data sets for use in scientific analyses such as: species distribution analysis,species richness and diversity studies, species occurrence studiesh, historical analysis, and other spatio-temporal analyses. This pack contains: - The Taxonomic Data Refinement (Integrated) Workflow (.t2flow file) - Its (version) dependent libraries - Relevant documentation, including diagrammatic representation of the workflow - Example data files , including input and output data. For more information on BioVeL's Data Refinement Workflow, please click here" with a link to http://www.biovel.eu/index.php/workflows/data-refinement-wf This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL : http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359
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| 10. |
- Sonja, Holl, et al.
(författare)
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On specifying and sharing scientific workflow optimization results using research objects
- 2013
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Ingår i: Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science. - Denver, Colorado : ACM. - 9781450325028
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Konferensbidrag (refereegranskat)abstract
- Reusing and repurposing scientific workflows for novel scientific experiments is nowadays facilitated by workflow repositories. Such repositories allow scientists to find existing workflows and re-execute them. However, workflow input parameters often need to be adjusted to the research problem at hand. Adapting these parameters may become a daunting task due to the infinite combinations of their values in a wide range of applications. Thus, a scientist may preferably use an automated optimization mechanism to adjust the workflow set-up and improve the result. Currently, automated optimizations must be started from scratch as optimization meta-data are not stored together with workflow provenance data. This important meta-data is lost and can neither be reused nor assessed by other researchers. In this paper we present a novel approach to capture optimization meta-data by extending the Research Object model and reusing the W3C standards. We validate our proposal through a real-world use case taken from the biodivertsity domain, and discuss the exploitation of our solution in the context of existing e-Science infrastructures.
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