System and method for network association inference, validation and pruning based on integrated constraints from diverse data

A computer-implemented method to combine diverse experimental data sets and to infer a network out of such data, the method comprising the steps of:

• a. designating one of the data sets as primary and the rest as secondary,
• b. analyzing the secondary data sets to obtain a connection matrix out of the secondary data sets by using statistical methods comprising R-square measures, clustering, correlation studies and mutual information measures,
• c. evolving a population of models and choosing a model from the population by proposing a population of plural nodes and connections from representation and representing the population by strings of characters and associated trees, with the string representation including characters representing data entities as well as a choice of mathematical operators,
• d. evaluating the values of the associated trees by integrating or iterating differential or difference equations along branches of the trees for a candidate in the population,
• e. assigning a fitness measure to each candidate based on i) presence of known motifs in the network, ii) stability of the network as evaluated by a linear stability analysis, iii) statistical measure of correlations in the data and iv) consistency with the prior known connections in the network, and
• f. incorporating penalties obtained from connection matrices in step b) into the fitness measure to arrive at an initial guess population consistent with the connections from the experimental data sets.