Every living being has a genetic code and a set of genes, which are needed to produce proteins starting from coded pieces of information. Genes are necessary for life and maintenance of organisms and are expressed inside cells: the contained information is transcribed and translated into proteins.

This gene expression phenomenon is based on a complex chain of events in which some particular proteins act on genes regions and can be simplified through a causal relationship between two genes. Causality is a kind of cause-and-effect binding between two variables: it means that the occurrence of the one is the cause of the appearance of the other.

Gene expression information is usually represented in Gene Regulatory Networks (GRN), which use edges to indicate the causal relationship between two genes. This representation is very useful to predict and manipulate the behavior of a system.

Every GRN can be expanded in order to add or suggest new genes related to the ones already known; this allows for amplification of the research and the analysis of a network. However, there are just a few methods available to perform the expansion, which is still an open challenge in the Bioinformatics world.

The project gene@home is meant to perform the GRN expansion and exploits an algorithm called PC-IM. It is an iterative implementation of the PC algorithm, which finds a gene network and studies its causal relationships, aimed to estimate if a list of new genes can have a causal relationship with an already known GRN. In particular, the new genes are partitioned in blocks and merged with the GRN; afterwards the PC is applied on each block to look for new possible relationships. At the end of the process the algorithm self-evaluates its performance, and based on this decides the final network to return as an output.