Relevant bibliographies by topics / Bioinformatics Visualization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bioinformatics Visualization'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Bioinformatics Visualization"

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Jiansi Ren, Jiantao Lu, Lizhe Wang, and Dan Chen. "Data Visualization in Bioinformatics." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 22 (2012): 157–65. http://dx.doi.org/10.4156/aiss.vol4.issue22.20.

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Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: interactive visualization of scRNA-seq data." Bioinformatics 36, no. 7 (2019): 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.

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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.
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Aladağ, Ahmet Emre, Cesim Erten, and Melih Sözdinler. "Reliability-Oriented bioinformatic networks visualization." Bioinformatics 27, no. 11 (2011): 1583–84. http://dx.doi.org/10.1093/bioinformatics/btr178.

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North, Chris, Theresa-Marie Rhyne, and Karen Duca. "Bioinformatics Visualization: Introduction to the Special Issue." Information Visualization 4, no. 3 (2005): 147–48. http://dx.doi.org/10.1057/palgrave.ivs.9500103.

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Song, Cheng Long, Chen Zou, Wen Ke Wang, and Si Kun Li. "An Integrated Framework for Biological Data Visualization." Advanced Materials Research 846-847 (November 2013): 1145–48. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1145.

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In the field of bioinformatics visualization, integrating software and data in different levels is the development trend. This paper presents an integration framework for biomolecular structure and genome sequences visualization. The framework can effectively support the data and software interoperability of biomolecular structure / genome sequences visualization. Based on the framework, we developed an integrated visualization system, which provides some new comprehensive visualization functions. Preliminary trial showed that the framework has a good prospect in the research of bioinformatics.
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Gonnella, Giorgio, Niklas Niehus, and Stefan Kurtz. "GfaViz: flexible and interactive visualization of GFA sequence graphs." Bioinformatics 35, no. 16 (2018): 2853–55. http://dx.doi.org/10.1093/bioinformatics/bty1046.

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Abstract Summary The graphical fragment assembly (GFA) formats are emerging standard formats for the representation of sequence graphs. Although GFA 1 was primarily targeting assembly graphs, the newer GFA 2 format introduces several features, which makes it suitable for representing other kinds of information, such as scaffolding graphs, variation graphs, alignment graphs and colored metagenomic graphs. Here, we present GfaViz, an interactive graphical tool for the visualization of sequence graphs in GFA format. The software supports all new features of GFA 2 and introduces conventions for their visualization. The user can choose between two different layouts and multiple styles for representing single elements or groups. All customizations can be stored in custom tags of the GFA format itself, without requiring external configuration files. Stylesheets are supported for storing standard configuration options for groups of files. The visualizations can be exported to raster and vector graphics formats. A command line interface allows for batch generation of images. Availability and implementation GfaViz is available at https://github.com/ggonnella/gfaviz Supplementary information Supplementary data are available at Bioinformatics online.
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Ágg, Bence, Andrea Császár, Máté Szalay-Bekő, et al. "The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein–protein interaction and signalling networks." Bioinformatics 35, no. 21 (2019): 4490–92. http://dx.doi.org/10.1093/bioinformatics/btz257.

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Abstract Motivation Network visualizations of complex biological datasets usually result in ‘hairball’ images, which do not discriminate network modules. Results We present the EntOptLayout Cytoscape plug-in based on a recently developed network representation theory. The plug-in provides an efficient visualization of network modules, which represent major protein complexes in protein–protein interaction and signalling networks. Importantly, the tool gives a quality score of the network visualization by calculating the information loss between the input data and the visual representation showing a 3- to 25-fold improvement over conventional methods. Availability and implementation The plug-in (running on Windows, Linux, or Mac OS) and its tutorial (both in written and video forms) can be downloaded freely under the terms of the MIT license from: http://apps.cytoscape.org/apps/entoptlayout. Supplementary information Supplementary data are available at Bioinformatics online.
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Milne, I., P. Shaw, G. Stephen, et al. "Flapjack--graphical genotype visualization." Bioinformatics 26, no. 24 (2010): 3133–34. http://dx.doi.org/10.1093/bioinformatics/btq580.

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Sidiropoulos, Konstantinos, Guilherme Viteri, Cristoffer Sevilla, et al. "Reactome enhanced pathway visualization." Bioinformatics 33, no. 21 (2017): 3461–67. http://dx.doi.org/10.1093/bioinformatics/btx441.

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Ullah, Ehsan, Michaël Aupetit, Arun Das, et al. "KinVis: a visualization tool to detect cryptic relatedness in genetic datasets." Bioinformatics 35, no. 15 (2018): 2683–85. http://dx.doi.org/10.1093/bioinformatics/bty1028.

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Abstract Motivation It is important to characterize individual relatedness in terms of familial relationships and underlying population structure in genome-wide association studies for correct downstream analysis. The characterization of individual relatedness becomes vital if the cohort is to be used as reference panel in other studies for association tests and for identifying ethnic diversities. In this paper, we propose a kinship visualization tool to detect cryptic relatedness between subjects. We utilize multi-dimensional scaling, bar charts, heat maps and node-link visualizations to enable analysis of relatedness information. Availability and implementation Available online as well as can be downloaded at http://shiny-vis.qcri.org/public/kinvis/. Supplementary information Supplementary data are available at Bioinformatics online.
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Dissertations / Theses on the topic "Bioinformatics Visualization"

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Rohrschneider, Markus. "Visualization of Metabolic Networks." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-160528.

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The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements.
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McDowell, Graeme S. V. "Advancing Lipidomic Bioinformatics: Visualization and phosphoLipid IDentification (VaLID)." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32203.

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Lipidomics is a relatively new field under the heading of systems biology. Due to its infancy, the field suffers from significant ‘growing pains’, one of which is the lack of bioinformatic analytic resources that other “-omics” fields enjoy. Here, I describe the creation and validation of the glycerophospholipid identification program VaLID. Using an in silico approach, we generated a comprehensive database containing all of the glycerophospholipids within multiple sub-classes: those containing chains of 0 to 30 carbons with up to 6 unsaturations and various linkages. Using Java, I created a web- based computer interface with a search engine and a visualization tool to access this database. In comparing results to current programs, I found that VaLID consistently contained more identity predictions than did the current gold standard LipidMAPS. Results from several tests with real datasets confirm that VaLID is more than capable as a phospholipid identification tool for use in lipidomics.
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Rönnbrant, Anders. "Implementing a visualization tool for myocardial strain tensors." Thesis, Linköping University, Department of Biomedical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5173.

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<p>The heart is a complex three-dimensional structure with mechanical properties that are inhomogeneous, non-linear, time-variant and anisotropic. These properties affect major physiological factors within the heart, such as the pumping performance of the ventricles, the oxygen demand in the tissue and the distribution of coronary blood flow.</p><p>During the cardiac cycle the heart muscle tissue is deformed as a consequence of the active contraction of the muscle fibers and their relaxation respectively. A mapping of this deformation would give increased understanding of the mechanical properties of the heart. The deformation induces strain and stress in the tissue which are both mechanical properties and can be described with a mathematical tensor object.</p><p>The aim of this master's thesis is to develop a visualization tool for the strain tensor objects that can aid a user to see and/or understand various differences between different hearts and spatial and temporal differences within the same heart. Preferably should the tool be general enough for use with different types of data.</p>
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Ding, Hao. "Visualization and Integrative analysis of cancer multi-omics data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1467843712.

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Liu, Feng. "Platform Independent Real-Time X3D Shaders and their Applications in Bioinformatics Visualization." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/24.

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Since the introduction of programmable Graphics Processing Units (GPUs) and procedural shaders, hardware vendors have each developed their own individual real-time shading language standard. None of these shading languages is fully platform independent. Although this real-time programmable shader technology could be developed into 3D application on a single system, this platform dependent limitation keeps the shader technology away from 3D Internet applications. The primary purpose of this dissertation is to design a framework for translating different shader formats to platform independent shaders and embed them into the eXtensible 3D (X3D) scene for 3D web applications. This framework includes a back-end core shader converter, which translates shaders among different shading languages with a middle XML layer. Also included is a shader library containing a basic set of shaders that developers can load and add shaders to. This framework will then be applied to some applications in Biomolecular Visualization.
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Wang, Wei. "Visualization of Clinically Annotated Electrophysiological Data for Multi-Center Sleep Studies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1436288274.

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Shi, Jieming. "Novel bioinformatics tools for miRNA-Seq analysis, RNA structure visualization, and genome-wide repeat detection." Miami University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=miami15003113547315.

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Indukuri, Kiran Kumar. "Fusion: a Visualization Framework for Interactive Ilp Rule Mining With Applications to Bioinformatics." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/36326.

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Microarrays provide biologists an opportunity to find the expression profiles of thousands of genes simultaneously. Biologists try to understand the mechanisms underlying the life processes by finding out relationships between gene-expression and their functional categories. Fusion is a software system that aids the biologists in performing microarray data analysis by providing them with both visual data exploration and data mining capabilities. Its multiple view visual framework allows the user to choose different views for different types of data. Fusion uses Proteus, an Inductive Logic Programming (ILP) rule finding algorithm to mine relationships in the microarray data. Fusion allows the user to explore the data interactively, choose biases, run the data mining algorithms and visualize the discovered rules. Fusion has the capability to smoothly switch across interactive data exploration and batch data mining modes. This optimizes the knowledge discovery process by facilitating a synergy between the interactivity and usability of visualization process with the pattern-finding abilities of ILP rule mining algorithms. Fusion was successful in helping biologists better understand the mechanisms underlying the acclimatization of certain varieties of Arabidopsis to ozone exposure.<br>Master of Science
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Wang, Jeremy R. "Analysis and Visualization of Local Phylogenetic Structure within Species." Thesis, The University of North Carolina at Chapel Hill, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3562960.

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<p> While it is interesting to examine the evolutionary history and phylogenetic relationship between species, for example, in a sort of "tree of life", there is also a great deal to be learned from examining population structure and relationships within species. A careful description of phylogenetic relationships within species provides insights into causes of phenotypic variation, including disease susceptibility. The better we are able to understand the patterns of genotypic variation within species, the better these populations may be used as models to identify causative variants and possible therapies, for example through targeted genome-wide association studies (GWAS). My thesis describes a model of local phylogenetic structure, how it can be effectively derived under various circumstances, and useful applications and visualizations of this model to aid genetic studies. </p><p> I introduce a method for discovering phylogenetic structure among individuals of a population by partitioning the genome into a minimal set of intervals within which there is no evidence of recombination. I describe two extensions of this basic method. The first allows it to be applied to heterozygous, in addition to homozygous, genotypes and the second makes it more robust to errors in the source genotypes. </p><p> I demonstrate the predictive power of my local phylogeny model using a novel method for genome-wide genotype imputation. This imputation method achieves very high accuracy&mdash;on the order of the accuracy rate in the sequencing technology&mdash;by imputing genotypes in regions of shared inheritance based on my local phylogenies. </p><p> Comparative genomic analysis within species can be greatly aided by appropriate visualization and analysis tools. I developed a framework for web-based visualization and analysis of multiple individuals within a species, with my model of local phylogeny providing the underlying structure. I will describe the utility of these tools and the applications for which they have found widespread use.</p>
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Baker, Frazier N. "Mining and Visualization of Amino Acid Coevolution Data." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1571061614939124.

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Books on the topic "Bioinformatics Visualization"

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Biodata mining and visualization: Novel approaches. World Scientific, 2010.

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International Symposium on Visual Computing (6th 2010 Las Vegas, Nev.). Advances in visual computing: 6th international symposium, ISVC 2010, Las Vegas, NV, USA, November 29 - December 1, 2010 : proceedings. Springer, 2010.

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P, Lévy Pierre, ed. Pixelization paradigm: First Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006 : revised selected papers. Springer, 2007.

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Richard, Boyle, Parvin Bahram, Koracin Darko, et al., eds. Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers, Part II. Springer Berlin Heidelberg, 2012.

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Burkowski, Forbes J. Computational and Visualization Techniques for Structural Bioinformatics Using Chimera. Taylor & Francis Group, 2014.

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Burkowski, Forbes J. Computational and Visualization Techniques for Structural Bioinformatics Using Chimera. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b17306.

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Computational and Visualization Techniques for Structural Bioinformatics Using Chimera Chapman HallCRC Mathematical Computational Biology. CRC Press, 2013.

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Boyle, Richard, George Bebis, Sabine Coquillart, et al. Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I. Springer, 2013.

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Xu, Kai, Jing Yang, Richard Boyle, et al. Advances in Visual Computing: 13th International Symposium, ISVC 2018, Las Vegas, NV, USA, November 19 – 21, 2018, Proceedings. Springer, 2018.

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Chen, Min, Richard Boyle, George Bebis, et al. Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part II. Springer, 2013.

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Book chapters on the topic "Bioinformatics Visualization"

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Schreiber, Falk. "Visualization." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_23.

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Tate, John. "Molecular Visualization." In Structural Bioinformatics. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471721204.ch7.

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Kaps, A., K. Heumann, D. Frishman, M. Bahr, and H. W. Mewes. "Visualization and analysis of the complete yeast genome." In Bioinformatics. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0033216.

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Sumner, Lloyd W., Ewa Urbanczyk-Wochniak, and Corey D. Broeckling. "Metabolomics Data Analysis, Visualization, and Integration." In Plant Bioinformatics. Humana Press, 2005. http://dx.doi.org/10.1007/978-1-59745-535-0_20.

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Kerren, Andreas, and Falk Schreiber. "Network Visualization for Integrative Bioinformatics." In Approaches in Integrative Bioinformatics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41281-3_7.

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Seo, Jinwook, and Ben Shneiderman. "Multidimensional Analysis and Visualization on Large Biomedical Data." In Statistical Bioinformatics. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470567647.ch7.

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Martz, Eric. "3D Molecular Visualization with Protein Explorer." In Introduction to Bioinformatics. Humana Press, 2003. http://dx.doi.org/10.1007/978-1-59259-335-4_32.

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De Paolis, Lucio Tommaso. "Augmented Visualization and Touchless Interaction with Virtual Organs." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78759-6_12.

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Berestneva, Olga G., Olga V. Marukhina, Sergey V. Romanchukov, and Elena V. Berestneva. "Visualization and Cognitive Graphics in Medical Scientific Research." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17935-9_39.

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Flynn, Emily, and Ileana Streinu. "Consistent Visualization of Multiple Rigid Domain Decompositions of Proteins." In Bioinformatics Research and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6_13.

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Conference papers on the topic "Bioinformatics Visualization"

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Hossain, Shammamah. "Visualization of Bioinformatics Data with Dash Bio." In Python in Science Conference. SciPy, 2019. http://dx.doi.org/10.25080/majora-7ddc1dd1-012.

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Havre, S. L., B. J. Webb-Robertson, A. Shah, C. Posse, B. Gopalan, and F. J. Brockma. "Bioinformatic insights from metagenomics through visualization." In 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05). IEEE, 2005. http://dx.doi.org/10.1109/csb.2005.19.

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Karve, Aneesh, and Michael Gleicher. "Glyph-based Overviews of Large Datasets in Structural Bioinformatics." In 11th International Conference Information Visualization - Supplements. IEEE, 2007. http://dx.doi.org/10.1109/iv.2007.150.

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Besancon, Camille, Alexandre Guillot, Sebastien Blaise, et al. "New visualization of dynamical flexibility of N-Glycans: Umbrella Visualization in UnityMol." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621256.

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"ACM Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBioinformatics)." In 2011 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2011. http://dx.doi.org/10.1109/biovis.2011.6094036.

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Zheng, Lin, Carlos Correa, and Kwan-Liu Ma. "Visibility guided multimodal volume visualization." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732506.

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"3D VISUALIZATION OF HAPLOTYPE RISK MAPS." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003708502750280.

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Raiford, Douglas W., Dan E. Krane, Travis E. Doom, and Michael L. Raymer. "Isolation and Visualization of Codon Usage Biases." In 2006 IEEE Symposium on Bioinformatics and Bioengineering. IEEE, 2006. http://dx.doi.org/10.1109/bibe.2006.253332.

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"Visualization of Bioinformatics Workflows for Ease of Understanding and Design Activities." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004195301160121.

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"HOLY-II: IMPROVED HIERARCHICALLY ORGANIZED LAYOUT FOR VISUALIZATION OF BIOCHEMICAL COMPLEX PATHWAYS." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002725501350140.

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Reports on the topic "Bioinformatics Visualization"

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Chain, Patrick Sam Guy. LANL activities in: genomics, bioinformatics, microbiome research, data visualization and biosurveillance. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493008.

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