Machine learning in cell identity, fate decision, and lineage prediction

Summary

Multi-omics and single-cell omics approaches are transforming our understanding of stem cell-based regeneration of tissues and organs on the individual cell level. By generating and leveraging the high-throughput and large-scale omic data generated in our lab as well as those that are publicly available, we aim to develop machine learning and statistical modelling methods to characterise the identities of various stem cells and subsequently predict their fate and lineages.

Supervisor(s)

Dr Pengyi Yang

Research Location

School of Mathematics and Statistics

Program Type

PHD

Synopsis

In this project, we will be working on developing state-of-the-art machine learning algorithms that could integrate “trans-omics” data that cut across multiple regulatory layers (cell signalling, transcriptional, translational, and epigenomic layers) for reconstructing trans-regulatory networks. We will next identify the key nodes of these networks for finding key determinants that define cell identities and cell fate decisions. The knowledge and the predictive power acquired from your work will be transformative for next-generation machine learning-based precision stem cell therapy.

Additional Information

Associated Scholarship is available for this project, please see it here (https://www.cmri.org.au/Research/For-Students/CMRI-PhD-research-award). 

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:  

  • Confidential disclosure and registration of a disability that may hinder your performance in your degree;  
  • Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);  
  • Ability to perform independently and/or with minimal supervision;  
  • Ability to undertake certain physical tasks (e.g. heavy lifting);  
  • Ability to undertake observatory, sensory and communication tasks; 
  • Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);  
  • Ability to work in confined spaces or at heights;  Ability to operate heavy machinery (e.g. farming equipment);  
  • Hold or acquire an Australian driver’s licence;  
  • Hold a current scuba diving license;  Hold a current Working with Children Check;  
  • Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)   

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

Machine learning, deep learning, statistical modelling, single-cell omics, multi-omics, Stem cells, cell identity, Cell fate, cell lineage, Cell Differentiation, Stem Cell Therapy

Opportunity ID

The opportunity ID for this research opportunity is: 2685

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