Using heuristics in the inference of genetic networks
The arrival of microarray technology has produced a lot of expression profiles of genes. The amount of data now available is so huge that new alternate and efficient methods are needed to analyse it. One of the approaches that have been taken is the use of reverse engineering to build up a picture of how the genes are interacting, where one of the obstacles is the amount of calculations needed. Liang et al. (1998) introduced an algorithm called REVEAL, where reverse engineering with entropy and mutual information are used in an attempt to generate the rules of regulation in genetic networks.In this dissertation it was investigated if it was possible to compliment the REVEAL algorithm with heuristics. The heuristic approach probed consists of setting a threshold on mutual information values, thereby dismissing combinations of input genes producing values below the threshold value as being non-relevant.Four experiments were performed, where each consisted of a different combination of rule complexity, size of network and number of inputs per gene tested.The findings of this study are that applying a threshold on mutual information is a realistic option that can reduce the number of calculations and also act as a filter that divides the important information from the irrelevant information. However this method has its limitations; since it is not known in advance where to place the threshold it will always be a chance that true connections fall below the threshold and therefore will be disregarded and not further analysed.
School:Högskolan i Skövde
Source Type:Master's Thesis
Keywords:entropy genetic networks mutual information reveal
Date of Publication:02/15/2008