Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms

In this study, we introduce a Genetic Algorithm-Recurrent Neural Network (GA-RNN) hybrid method for finding feed-forward regulated genes when given some transcription factors to construct cancer-related regulatory modules in human cancer microarray data. This hybrid approach focuses on the construction of various kinds of regulatory modules, that is, Recurrent Neural Network has the capability of controlling feed-forward and feedback loops in regulatory modules and Genetic Algorithms provide the ability of global searching of common regulated genes. This approach unravels new feed-forward connections in regulatory models by modified multi-layer RNN architectures. We also validate our approach by demonstrating that the connections in our cancer-related regulatory modules have been most identified and verified by previously-published biological documents.The major contribution provided by this approach is regarding the chain influences upon a set of genes sequentially. In addition, this inverse modeling correctly identifies known oncogenes and their interaction genes in a purely data-driven way.A regulatory module is a set of genes that is regulated or co-regulated by one or more common transcription factors (TFs). A TF is a protein that binds to a cis-regulatory element (e.g. an enhancer, a TATA box) and thereby, directly or indirectly, positively or negatively affects the initiation of transcription of regulated genes. A cancer-related regulatory module is a set of genes (oncogenes or tumor suppressor genes) that is regulated by one ore more common TFs. Modeling the cancer-related regulatory modules of the cell division cycle in human cells is a critical and fundamental step toward understanding cancers. The aim of this paper is not only to drive cancer-related regulatory modules, but also to identify the relationships of regulations between genes that fit the feed-forward or feedback influences. A feed-forward regulatory module, contains a TF that controls a second