Quantitative assessment of biological impact using overlap methods

Abstract

Scores for particular network models (those having a source node connected to a set of measurable downstream nodes via causal edges) are computed across multiple networks by accounting for an overlap between these models in a manner that reduces cross-network redundancy and increases the specificity of the network models for the network in which they are found. According to another aspect, a meta-network model is created for networks by accounting for the occurrence of network models that are found in multiple networks in a manner that reduces the redundancy across networks and that increases the specificity of the network model score. Preferably, this process provides additional weighting factors for each node in the network model.

Description

TECHNICAL FIELD[0001]This disclosure relates generally to methods and techniques for characterizing the response of biological networks.BACKGROUND OF THE RELATED ART[0002]Networks provide a powerful framework for describing complex systems in many different areas, ranging from natural and social sciences to computer and electrical engineering. Their quantitative analysis is based on the concepts and properties studied in the mathematical field of graph theory. Leveraging this knowledge can help address challenging problems that arise in concrete situations.[0003]Signed graphs are used in a variety of disciplines including systems biology, where a signed edge relating two nodes may represent the positive or negative regulatory relationship between two biological entities within a network. Recent advances in experimental and computational techniques have enabled systems-wide measurement of biological entities such as gene expressions or protein activities, and facilitated their integr...

AI Technical Summary

Benefits of technology

This patent describes a way to analyze large sets of data to identify specific patterns of activity in different networks. The method involves using a scoring system to measure the impact of a particular event on a set of genes or other molecules. The results from multiple networks are combined to create a more accurate and specific model of the overall impact of the event. This approach helps to reduce the noise and redundancy in the data, making it easier to identify important connections and patterns. A key technical effect of this patent is to provide a more reliable and accurate way to analyze complex data sets and identify important patterns of activity.

Problems solved by technology

Furthermore, while it may not be possible to easily measure some of the nodes in a network, it may be possible to obtain a score for some of these nodes that have an associated signature of measurements.

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