Changes in properties of the transport network graph when using simplification algorithms

In the course of solving problems of managing and planning the development of large transport networks, a big amount of heterogeneous data from various sources is accumulated. The methods of graph theory are used to systematize information, process it, and calculate network characteristics. However, the problem arises of the enormous complexity and duration of computations over graphs of large networks. One of the approaches to solve this problem is the simplification of the original graph. In our report we study the influence of processing and basic graph simplification on the initial properties of the network. © 2019 Author(s).Russian Foundation for Basic Research, RFBR: number17-08-01123-aThe investigation was funded by RFBR, project number17-08-01123-a

Kirchhoff's Circuit Law Applications to Graph Simplification in Search Problems

This paper proposes a new analysis of graph using the concept of electric potential, and also proposes a graph simplification method based on this analysis. Suppose that each node in the weighted-graph has its respective potential value. Furthermore, suppose that the start and terminal nodes in graphs have maximum and zero potentials, respectively. When we let the level of each node be defined as the minimum number of edges/hops from the start node to the node, the proper potential of each level can be estimated based on geometric proportionality relationship. Based on the estimated potential for each level, we can re-design the graph for path-finding problems to be the electrical circuits, thus Kirchhoff's Circuit Law can be directed applicable for simplifying the graph for path-finding problems

Integrating OMIM and IntAct Data for the Analysis of Gene-Phenotype Interactions in Complex Diseases: a Linux-based Computational Tool for Network Analysis

The field of genetics is constantly evolving. New advances in bioinformatics and computational approaches are leading to exciting new developments in our ability to treat and prevent diseases. Computational genetics provides valuable insights into the complex mechanisms and layers of biological communication that shape an organism\u27s phenotype. Understanding these mechanisms is critical to advancing human health.The study of diseases in genetics requires a comprehensive understanding of the interactions between various biological processes, including gene expression, protein synthesis, RNA, metabolism, and cell-cell communication. To effectively address the root causes of such diseases, multi-disciplinary approaches that integrate information from different levels of biological organization are increasingly needed. Network analysis, also known as graph theory analysis, provides a solution by allowing the visualization and quantification of these relationships.This paper focuses on the features and functions of GenoPheno, a program I designed to automate the construction and analysis of genotype, phenotype, and protein interaction networks constructed from clinical and genetic disease data retrieved from the OMIM (Online Mendelian Inheritance in Man) database and protein interaction data retrieved from the IntAct database. I aim to demonstrate the potential utility of GenoPheno in facilitating the exploration of genetic and protein relationships underlying disease progression