Algorithm for Ranking Master Regulators in Gene Networks

Bioinformatics Program Quantifies Influence by Assigning Importance Scores to Nodes in Networks

This network ranking system uses a computational algorithm based on a set of parameters to generate an importance score for each node in a network, thereby establishing the means to rank and identify master regulators, in particularly transcription factors, in complex gene networks. Bioinformatics programs like this are important tools in a number of scientific fields including computational biology, and the bioinformatics market is expected to grow to $12.8 billion by 2020. Often in gene networks, one gene regulates another gene, which regulates another gene, and so on. Additionally, most transcription factors bind to multiple binding sites in a cell’s genome. As a result, gene regulatory networks are complex and difficult to analyze. Another issue in computational biology is the desire to incorporate various parameters known individually to influence network ranking. Researchers at the University of Florida have developed nSCORE (network Systems Calculation of Optimal Ranking Engine), a network ranking framework, experimentally validated as highly accurate, that combines many existing parameters into a comprehensive algorithm that produces importance scores to plainly quantify the influence of regulators in any gene network. nSCORE can be applied to any field that involves the use of networks because it is designed to take any type of network and node statistics as inputs.

Application

Network analysis engine that determines a hierarchy of nodes according to the importance of each node in the network, maintaining compatibility to networks of all types (examples include analyses to identify genes responsible for cell type conversions, for disease drug targets, for drug response predictions in patients)

Advantages

• Generates an importance score for each node in a network, quantifying the importance of individual genes and identifying the master regulators in any type of gene network
• Incorporates limitless sets of existing parameters into one scoring framework, eliminating any need for prior knowledge about the gene network
• Takes any type of network and node statistics as inputs, broadening its use for similar analysis of any other network-based system
• Predicts scores with extremely high accuracy (e.g. predicts 3 of 4 Yamanaka factors used to convert normal cells to pluripotent stem cells), making it the best prediction so far

Technology

A number of network analysis measures reflect the influence of a node in a network, such as degree or “betweenness.” While varied approaches can isolate specific effects on other nodes in the network, nSCORE (network Systems Calculation of Optimal Ranking Engine) automatically synthesizes limitless sets of existing parameters that individually influence network properties and uses a computational algorithm to produce a node importance score drawn from all available node information. Because it takes as inputs data sets describing any network and node statistics, nSCORE is capable of ranking nodes in networks and node statistics of any type, whether known or unknown.