Tamu matlab help software#
Tamu matlab help code#

All major functions can be accessed from the menu and it is easy and simple for the end-users to load a network file, compute statistic measures for the network and its nodes, detect highly connected node clusters (or modules) using graph clustering algorithms, and evolve the networks.

SBEGUI is a figure window to which user-operated controls are organized as a drop-down menu to access core functions of the SBEToolbox ( Fig. The adjacency matrix is loaded into the variable named sbeG, and the node information is stored in a cell string vector sbeNode. It saves the network information on disk in a Matlab MAT-file for each working session as an n × n sparse adjacency matrix representing the network of n nodes. SBEToolbox can read and write network information in three commonly used network file formats: tab-delimited, SIF, and Pajek.

Tamu matlab help code#

1 Functions developed in native code can be classified into: (1) core functions that execute the tasks organized under SBEToolbox’s menu and (2) helper functions that automate repetitive tasks and assist core functions. SBEToolbox is implemented using a combination of native Matlab code and MEX/C functions based on the Boost Graph Library ( Fig.

Tamu matlab help software#

In the following sections, we describe how we designed Systems Biology and Evolution Toolbox (SBEToolbox), subsequently discuss various algorithms implemented for network analysis, and finally mention the advantages as well as potential drawbacks of our software and point out our future research roadmap. With such software, it is possible for users without programming expertise to directly relate specific biological interactions with the network properties and dynamics.

In our opinion, good network analysis software should include a sufficient number of graph algorithms being efficiently implemented as functions, which are flexible enough to be extended easily, and accessible through easy-to-use user interfaces (UIs). Network analysis software usually transforms the network data into a graph framework in order to take distinct advantages of being able to adopt techniques developed in graph theory, engineering, and computer science. The volumes of biological network data gathered in systems biology have outpaced their ability to assimilate them into real knowledge and the requirement of software tools for analyzing network data is continually growing. The complexity of biological systems represents an enormous intellectual challenge to researchers who mostly remain reliant on correlative approaches to biology.