Browsing by Subject "Visualization Software"

Now showing 1 - 3 of 3

Open Access
A layout algorithm for graphs with overlapping clusters = Kesişen kümelenmiş çizgeler için bir yerleştirme algoritması
(2014) Cengiz, Can Çağdaş
Graphs are often used for visualizing relational data such as social or biological networks. Numerous methods have been proposed for automatic layout of simple graphs. However, simple graphs are usually insufficient in displaying relational information, since relational information is often clustered. Clustering models traditionally assume that each data point belongs to one and only one cluster; however, in complex networks, these clusters often overlap. For effective visualization of clustered graphs, the nodes in the same cluster should be placed together, respecting general graph drawing criteria such as avoiding node-node overlaps, minimizing edge crossings, and minimizing the total drawing area. Clustered graph layout problem becomes even more challenging when cluster overlaps are allowed. Here, we present a new algorithm for automatic layout of graphs with overlapping clusters based on force directed layout approach. The graph is first divided into zones according to clusters and their intersections, and new additional forces are introduced to the traditional spring embedder algorithm to keep nodes in the same cluster together, trying to keep neighboring nodes in separate clusters at a safe distance. Spring constants had to be fine-tuned to achieve a fast and effective layout operation. The algorithm was implemented and validated within a new layout style named Cluster Layout in the layout module of ChiEd visualization tool.
Open Access
Methods and tools for visualization and management of SBGN process description maps
(2014) Sarı, Mecit
Graphs are commonly used to model relational information in many areas such as relational databases, software engineering, biological and social networks. In visualization of graphs, automatic layout, interactive editing and complexity management of crowded graphs are essential for effective utilization of underlying information. Advances in graphical user interfaces have given rise and value to interactive editing and diagramming techniques in graph visualization. As the size of the information to be visualized vastly increased, it became harder to analyze such networks, making use of relational information needed to be acquired. To overcome this problem, sophisticated and domain-specific complexity management techniques should be provided. The Systems Biology Graphical Notation (SBGN) has been developed over a number of years by biochemists and computer scientists to standardize visual representation of biochemical and cellular processes. SBGN introduces a concrete, detailed set of symbols for scientists to represent network of interactions, in a way that is not open to more than one interpretation. It also describes the manner, in which such graphical information should be interpreted. The SBGN Process Description (PD) language shows how entities are influenced by processes, which are represented by several reaction types in a biological pathway. It can be used to show all the molecular interactions taking place in a network of biochemical entities, with the same entity appearing multiple times in the same diagram. We developed methods and tools to effectively visualize and manage SBGNPD diagrams. Specifically, we introduced new algorithms for proper management of complexity of large SBGN-PD diagrams. These algorithms strive to keep SBGN-PD diagrams intact as complexity management takes places. In addition, we provided software components and web-based tools that implement these methods. These tools use state-of-the-art web technologies and libraries.
Open Access
Software tools for visual analysis of cancer genomics data in the context of pathways
(2016-08) Bahçeci, İstemi Rahman
Information visualization is concerned with effective visual presentation of abstract information, which reinforces human cognition. Graphs are structures that are well suited to represent relational information. Graph visualization is vital since the underlying relational information of the graph provides fine analysis and comprehension opportunities. Biological pathway visualization is one of the most popular areas, where graph visualization is highly favored. Interactive analysis and visualization of cancer related pathways in the context of genomic data, such as those available through the TCGA project, might reveal valuable information for scientists about disease conditions and potential causes. As the size and complexity of such cancer pathways and associated genomic data increase, exchangeable in-silico representations and their effective, enhanced visualizations, and complexity management become crucial for effective analysis of such data to potentially discover cause-effect relations. In this thesis, we designed and implemented software solutions to visualize cancer genomics data in the context of networks from simple gene interaction networks to process description diagrams within the cBioPortal for Cancer Genomics (cBioPortal). cBioPortal is a popular web portal, getting about 60.000 visits globally per month, providing visualization, analysis and download of largescale cancer genomics data sets. The network view in cBioPortal presents neighborhood of genes of interest. The alteration data is overlaid on the network with numerous ways to filter and manage complexity of the network (e.g. by alteration percentage or by type or source of the interactions). Upon demand, the user can obtain a more detailed, mechanistic view of the interactions among gene pairs, from Pathway Commons database with a live query using the SBGN process description notation. Finally, we also developed a new pathway visualization component, specifically for cancer pathways, using a uniform notation found in TCGA cancer publications. This tool also facilitates curation of pathways from scratch with support for collaborative editing.