Abstract: Embodiments of this invention include application of new inferential methods to analysis of complex biological information, including gene networks. New methods include modifications of Bayesian inferential methods and application of those methods to determining cause and effect relationships between expressed genes, and in some embodiments, for determining upstream effectors of regulated genes. Additional modifications of Bayesian methods include use of time course data and use of gene disruption data to infer causal relationships between expressed genes. Other embodiments include the use of bootstrapping methods and determination of edge effects to more accurately provide network information between expressed genes. Information about gene networks can be stored in a memory device and can be transmitted to an output device, or can be transmitted to remote location.

Abstract: The accurate estimation of gene networks from gene expression measurements is a major challenge in the field of Bioinformatics. We present a general approach to reduce the search space to a biologically meaningful subspace and to find optimal solutions within the subspace in linear time by using inferential models constrained by biologically relevant information. We showed the effectiveness of this approach in application to yeast and Bacillus subtilis data. Also, we provide systems and storage media adapted to provide and store data and results of gene network relationships.