Intelligent Learning and Verification of Biological Networks

Abstract

AbstractMachine learning and model checking are two types of intelligent computing techniques that have been widely used to study different complicated systems nowadays. It is well-known that the cellular functions and biological processes are strictly regulated by different biological networks, for example, signaling pathways and gene regulatory networks. The pathogenesis of cancers is associated with the dysfunctions of some regulatory networks or signaling pathways. A comprehensive understanding of the biological networks could identify cellular signatures and uncover hidden pathological mechanisms, and help develop targeted therapies for cancers and other diseases. In order to correctly reconstruct biological networks, statisticians and computer scientists have been motivated to develop many intelligent methods, but it is still a challenging task due to the complexity of the biological system and the curse of dimensionality of the high-dimensional biological data. In this work, we will review different machine learning algorithms and formal verification (model checking) techniques that have been proposed and applied in our previous work and discuss how to integrate these computational methods together to intelligently infer and verify complex biological networks from biological data. The advantages and disadvantages of these methods are also discussed in this work.