A major initiative in our group is to integrate trans-omics datasets generated by a state‐of‐the‐art mass spectrometer (MS) and next-generation sequencer (NGS) from various cell systems. We have now profiled various stem/progenitor cell differentiation processes using a combination of MS and NGS and have generated large-scale trans-omics datasets in these cell systems (see https://doi.org/10.1016/j.cels.2019.03.012. These data provide exciting research directions where we hypothesize that data integration across multiple omic layers is the key to a comprehensive understanding of the underlying biological systems.
A complimentary scholarship for this project may be available through a competitive process. To find out more, refer to the Faculty of Science Postgraduate Research Excellence Award and contact Dr Pengyi Yang directly.
School of Mathematics and Statistics
PHD
The aim of this PhD project is to develop computational methods for integrating and making sense of multi-layered omic datasets. Specifically, you will be developing and applying unsupervised, semi-supervised and supervised machine learning techniques and general data analytics for integrating and making sense trans-omics data that capturing the dynamics of stem and progenitor cell differentiation. Programming skill is essential for this project. Knowledge discovered from this project will translate into exciting biological findings and shed light on development, regeneration, and treatment for complex diseases and aging.
A complimentary scholarship for this project may be available through a competitive process. To find out more, refer to the Faculty of Science Postgraduate Research Excellence Award and contact Dr Pengyi Yang directly.
HDR Inherent Requirements
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:
The opportunity ID for this research opportunity is 2713