Graph-tool

Abstract: This paper describes a tool graph originally developed for the Faust environment. Faust is a scientific program development environment being implemented at the Center for Supercomputing Research and Development at the University of Illinois at Urbana-Champaign. The graph tool comprises two major components: the Graph Manager that implements an abstract graph data type, and the Graph Browser that handles the details of displaying a subgraph of a graph created through the Graph Manager. The Graph Browser displays graph views, where a graph view is a subgraph of its parent graph. The concept of graph views is analogous to the concept of views in the traditional database sense. Several graph views may simultaneously exist for a single parent graph, where each view's subgraph depends on the context of the application requesting the view. Goals of the graph tool, GMB, included providing an abstract graph data type for general use and animating graphs efficiently.

Abstract: The structured vocabulary that describes gene function, the gene ontology (GO), serves as a powerful tool in biological research. One application of GO in computational biology calculates semantic similarity between two concepts to make inferences about the functional similarity of genes. A class of term similarity algorithms explicitly calculates the shared information (SI) between concepts then substitutes this calculation into traditional term similarity measures such as Resnik, Lin, and Jiang-Conrath. Alternative SI approaches, when combined with ontology choice and term similarity type, lead to many gene-to-gene similarity measures. No thorough investigation has been made into the behavior, complexity, and performance of semantic methods derived from distinct SI approaches. We apply bootstrapping to compare the generalized performance of 57 gene-to-gene semantic measures across six benchmarks. Considering the number of measures, we additionally evaluate whether these methods can be leveraged through ensemble machine learning to improve prediction performance. Results showed that the choice of ontology type most strongly influenced performance across all evaluations. Combining measures into an ensemble classifier reduces cross-validation error beyond any individual measure for protein interaction prediction. This improvement resulted from information gained through the combination of ontology types as ensemble methods within each GO type offered no improvement. These results demonstrate that multiple SI measures can be leveraged for machine learning tasks such as automated gene function prediction by incorporating methods from across the ontologies. To facilitate future research in this area, we developed the GO Graph Tool Kit (GGTK), an open source C++ library with Python interface (github.com/paulbible/ggtk).

Abstract: Author(s): Bliss, Drew; Dillencourt, Michael B. | Abstract: GraphTool is an interactive tool for editing graphs and visualizing the execution and results of graph algorithms. It runs under both the SunView and X Windows environments and has a full window/mouse interface which is as similar as possible for the two windowing systems. In addition, there is a standalone program called the Wrapper which simulates the Graph-Tool interface without graphics for batch processing of graph algorithms. While the primary purpose of GraphTool is to provide a means for experimentally investigating the performance of graph algorithms, it has other useful features as well. It provides features for printing graphs in a visually appealing format, which makes it easier to prepare papers for publication. It also provides a facility for "animating" algorithms, which means that it can be used in computer assisted instruction (CAI) and for preparing video presentations of algorithms.

Abstract: All technological, biological and social networks can be represented as graphs. Therefore, graphs are utilised in simulation-based studies of new algorithms and protocols in various scientific fields. This paper presents gratis, an easy to use graph generator, that produces structures for the study of complex networks. The gratis application is optimized to generate realistic network topologies for Wireless Network Sensors (wsn), IoT implementations and many other types of networks. The gratis tool also provides analytical and visualisation capabilities for already existing graphs. More importantly, the gratis paltform is designed to build reproducible graphs that help scientists to validate previous research based on simulations.