Dynamics 4 Genomic Big Data

The immune response to viral infection is a dynamic process, which is regulated by an intricate network of many genes and their products.

Understanding the dynamics of this network will infer the mechanisms involved in regulating influenza infection and hence aid the development of antiviral treatments and preventive vaccines. There has been an abundance of literature regarding dynamic network construction, e.g., Hecker et al. (2009), Lu et al. (2011) and Wu et al. (2013).

My research involves the development of a new pipeline for dynamic network construction for high-dimensional time course gene expression data. This pipeline allows us to discern the fundamental underlying biological process and their dynamic features at genetic level.

The pipeline includes:

Novel statistical methods and modelling approaches have been developed for the implementation of this new pipeline, which include a new approach for the selection of the smoothing parameter, a new clustering approach and a new method for model selection for high-dimensional ODEs.


Carey, M., Wu, S., Gan, G. and Wu, H. (2016) 'Correlation-based iterative clustering methods for time course data: the identification of temporal gene response modules to influenza infection in humans'. Infectious Disease Modelling.


Song, J., Carey, M., Zhu, H., Miao, H., Ramırez, Juan and Wu, H. (2017) 'Identifying the dynamic gene regulatory network during latent HIV-1reactivation using high-dimensional ordinary differential equations'. International Journal of Computational Biology and Drug Design


Carey, M., Wu, S.,  Wu, H., 'A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection'. Statistical Methods in Medical Research


Frontiers in Functional Data Analysis

The Banff International Research Station, Canada

June 28th to July 3rd, 2015.

Fantastic workshop. Many thanks to Debashis Paul, Surajit Ray and David Ruppert for the invite.

In recent years, the field of functional data analysis has been widely used to answer science and policy questions, where the data are typically observed over time, space and other continuous variables. Current methodologies provide sophisticated computational techniques in solving complex problems in a wide range of application areas ranging from biomedical imaging, climate-environment interaction and unraveling networks evolving in time and space. After a period of prolific growth in computational techniques and methodological development, primarily motivated by diverse application areas, the time has come to consolidate the recent progress and provide a platform where researchers could exchange ideas and start collaboration on scientific projects and build a robust inferential framework for functional data analysis that takes into account the increasing complexities of the data.

This workshop is intended to bring together the leaders in this field, representatives of application areas, and promising young researchers to charter the path for future development in the field.