Visual Data Mining Tool Research Articles

Genome Context Viewer: visual exploration of multiple annotated genomes using microsynteny.

SummaryThe Genome Context Viewer is a visual data-mining tool that allows users to search across multiple providers of genome data for regions with similarly annotated content that may be aligned and visualized at the level of their shared functional elements. By handling ordered sequences of gene family memberships as a unit of search and comparison, the user interface enables quick and intuitive assessment of the degree of gene content divergence and the presence of various types of structural events within syntenic contexts. Insights into functionally significant differences seen at this level of abstraction can then serve to direct the user to more detailed explorations of the underlying data in other interconnected, provider-specific tools. Availability and implementationGCV is provided under the GNU General Public License version 3 (GPL-3.0). Source code is available at https://github.com/legumeinfo/lis_context_viewer.Supplementary information Supplementary data are available at Bioinformatics online.

CompNet: a GUI based tool for comparison of multiple biological interaction networks.

BackgroundNetwork visualization and analysis tools aid in better understanding of complex biological systems. Furthermore, to understand the differences in behaviour of system(s) under various environmental conditions (e.g. stress, infection), comparing multiple networks becomes necessary. Such comparisons between multiple networks may help in asserting causation and in identifying key components of the studied biological system(s). Although many available network comparison methods exist, which employ techniques like network alignment and querying to compute pair-wise similarity between selected networks, most of them have limited features with respect to interactive visual comparison of multiple networks.ResultsIn this paper, we present CompNet - a graphical user interface based network comparison tool, which allows visual comparison of multiple networks based on various network metrics. CompNet allows interactive visualization of the union, intersection and/or complement regions of a selected set of networks. Different visualization features (e.g. pie-nodes, edge-pie matrix, etc.) aid in easy identification of the key nodes/interactions and their significance across the compared networks. The tool also allows one to perform network comparisons on the basis of neighbourhood architecture of constituent nodes and community compositions, a feature particularly useful while analyzing biological networks. To demonstrate the utility of CompNet, we have compared a (time-series) human gene-expression dataset, post-infection by two strains of Mycobacterium tuberculosis, overlaid on the human protein-protein interaction network. Using various functionalities of CompNet not only allowed us to comprehend changes in interaction patterns over the course of infection, but also helped in inferring the probable fates of the host cells upon infection by the two strains.ConclusionsCompNet is expected to be a valuable visual data mining tool and is freely available for academic use from http://metagenomics.atc.tcs.com/compnet/ or http://121.241.184.233/compnet/Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1013-x) contains supplementary material, which is available to authorized users.

Tractome: a visual data mining tool for brain connectivity analysis

Diffusion magnetic resonance imaging data allows reconstructing the neural pathways of the white matter of the brain as a set of 3D polylines. This kind of data sets provides a means of study of the anatomical structures within the white matter, in order to detect neurologic diseases and understand the anatomical connectivity of the brain. To the best of our knowledge, there is still not an effective or satisfactory method for automatic processing of these data. Therefore, a manually guided visual exploration of experts is crucial for the purpose. However, because of the large size of these data sets, visual exploration and analysis has also become intractable. In order to make use of the advantages of both manual and automatic analysis, we have developed a new visual data mining tool for the analysis of human brain anatomical connectivity. With such tool, humans and automatic algorithms capabilities are integrated in an interactive data exploration and analysis process. A very important aspect to take into account when designing this tool, was to provide the user with comfortable interaction. For this purpose, we tackle the scalability issue in the different stages of the system, including the automatic algorithm and the visualization and interaction techniques that are used.

A Novel Visual Data Mining Module for the Geographical Information System gvSIG

The exploration of large GIS models containing spatio-temporal information is a challenge. In this paper we propose the integration of scientific visualization (ScVis) techniques into geographic information systems (GIS) as an alternative for the visual analysis of data. Providing GIS with such tools improves the analysis and understanding of datasets with very low spatial density and allows to find correlations between variables in time and space. In this regard, we present a new visual data mining tool for the GIS gvSIG. This tool has been implemented as a gvSIG module and contains several ScVis techniques for multiparameter data with a wide range of possibilities to explore interactively the data. The developed module is a powerful visual data mining and data visualization tool to obtain knowledge from multiple datasets in time and space. A real case study with meteorological data from Villa Clara province (Cuba) is presented, where the implemented visualization techniques were used to analyze the available datasets. Although it is tested with meteorological data, the developed module is of general application in the sense that it can be used in multiple application fields related with Earth Sciences.

Multi-Objective Design Exploration and its Applications

Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (−400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org.

GExMap: An Intuitive Visual Tool to Detect and Analyze Genomic Distribution in Microarray-generated Lists of Differentially Expressed Genes

High- throughput technologies, as DNA microarrays, generate a huge amount of data, which are difficult to interpret. Biologists require easy-to-use tools to analyze them and explore new scientific hypotheses. We propose a visual data mining tool to extract a new type of useful genomic information buried in gene lists generated by differential expression studies. We compare the genomic distribution that is observed within the gene list with the expected distribution, which is estimated from public genomic databases. An algorithm of research and a statistical test give reliable and optimal results. GExMap helps identify genomic regions that are enriched in genes whose differential expression is of potential interest for target diseases. This software is freely available20 and easily customized. Since sources are frequently updated, it offers tools for updates at any moment. GExMap is usable by any commercially and publicly available microarray platform. Furthermore, GExMap helps in interpretation by showing an ordered list for each gene ontology. Presently, GExMap can also be used to analyse gene lists not only from the Homo sapiens genome but also the Mus musculus and the Rattus norvegicus genomes.

InfVis − Platform-Independent Visual Data Mining of Multidimensional Chemical Data Sets

The tremendous increase of chemical data sets, both in size and number, and the simultaneous desire to speed up the drug discovery process has resulted in an increasing need for a new generation of computational tools that assist in the extraction of information from data and allow for rapid and in-depth data mining. During recent years, visual data mining has become an important tool within the life sciences and drug discovery area with the potential to help avoiding data analysis from turning into a bottleneck. In this paper, we present InfVis, a platform-independent visual data mining tool for chemists, who usually only have little experience with classical data mining tools, for the visualization, exploration, and analysis of multivariate data sets. InfVis represents multidimensional data sets by using intuitive 3D glyph information visualization techniques. Interactive and dynamic tools such as dynamic query devices allow real-time, interactive data set manipulations and support the user in the identification of relationships and patterns. InfVis has been implemented in Java and Java3D and can be run on a broad range of platforms and operating systems. It can also be embedded as an applet in Web-based interfaces. We will present in this paper examples detailing the analysis of a reaction database that demonstrate how InfVis assists chemists in identifying and extracting hidden information.