Developing novel machine learning algorithms for network biology

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In the past few decades, network-based approaches to explore biological processes have been extensively studied. Many of these methods have been investigated only in  relatively small-scale benchmark data sets. Currently, with the advances in high-throughput technologies, more genome-scale genomic data have become readily accessible. An interesting research field is how to mine these high-dimensional and voluminous data. Network-based computational approaches have been regarded as a promising technique to build the best possible learning systems to understand different biological and pathological processes that interact in a complex biological network. However computational complexity of their learning process is much higher than other alternative learning schemes in many biological applications. In this proposal I focus on three major areas: (1) Jointly predict protein functions and disease gene types in biological networks. Although network-based gene/protein function and disease gene predictions have been widely studied, these two tasks are solved independently. I will implement a computer learning system to joint prediction of the two label types in a biological network; (2) Identify network biomarker. Different approaches have been developed for selecting gene and protein biomarkers. However, many studies have shown these types of biomarkers are not reproducible when they are applied in other independent studies. I will develop novel computer systems to identify biologically and functionally relevant subnetwork biomarkers, which is thought to be more robust; and (3) Infer sample-specific activation patterns of subnetworks under different biological conditions from biological data. To understand biological mechanisms using the identified subnetwork biomarkers, I will develop computational methods to infer patterns of subnetwork activation and co-activation. This research program will advance large-scale network-based automatic knowledge discovery technologies for Canadian and international IT sectors.