Inferring Gene Regulatory Networks from Multi-omics Data (Liang Lan et al.)

Gene Regulatory Networks (GRN) play a key role on controlling the expression levels of mRNA and Proteins. It provides us in-depth understanding of complex biological processes. In most of existing research, the gene regulatory networks are inferred based on a single type of genomic data (e.g., gene expression data). However, gene expression is the final product of multiple complex biological processes, such as transcription, methylations and histone modifications. Due to the recent advances of high-throughput ‘omics’ technologies, multiple types of genomic data are publicly available.

In this project, we will develop novel algorithms that are capable of accurately learning multi-level regulatory networks from multiple ‘omics’ data. Key issues to address include heterogeneous data integration, heterogeneous network modeling and dealing with noise and uncertainty in the ‘omics’ data.

For further information on this research topic, please contact Dr. Liang Lan.