Abstract: A network association mining algorithm and associated methods are presented which accepts data from biological and other experiments and automatically produces a network and attempts to explain the behavior of the biological or other system underlying the data using evolutionary techniques. The model and associated methods aim to identify the inter-relationships consistent with data and other prior knowledge supplied to the system. The network is represented in terms of coupled dynamical system. These dynamical systems are represented by differential or difference equations to educe these dynamical systems and coupling between them, an evolutionary algorithm is used. The output of the linkage finder could assist scientists to better understand the systems underlying and to guess at surrogate data.

Abstract: A network inference and validation engine is presented which combines data of different types into a network associations' inference and performs validation of existing networks based on constraints from several data sets or previously known linkages. The engine would assist scientists to integrate information from various sources into a network of association, validate previously known associations against the supplied constraint data sets and prune or modify existing connections based on subsequent experiments.