Machine learning of -omic data for discovery of disease processes.

Summary

This project builds on a number of recent findings and aims to bring together omics data related to disease and machine learning for the prediction of transcriptional regulators involved in disease. This has the potential to provide new insight into the processes involved in and causality of disease.

Supervisor(s)

Dr Ashley Waardenberg, Associate Professor Jonathan Arthur

Research Location

Westmead - Childrens Medical Research Institute

Program Type

Masters/PHD

Synopsis

Gene regulation is largely under the control of transcription factors - proteins that bind to DNA and engage the cellular programs responsible for normal developmental processes (for a review of gene regulatory networks read: Waardenberg AJ et al. 2014 – “Genetic network governing heart development”).

Recently, it was shown that disease causing mutations in transcription factors alters their genome wide binding patterns that ultimately lead to perturbed gene expression (Bouveret R, Waardenberg, AJ, et al. eLife, 2015). Stability of binding and experimental setting can however influence binding and variability can be observed, thus leading to questions surrounding reproducibility of binding (Waardenberg AJ et al. BMC Bioinformatics 2015). This is an important consideration when trying to determine the processes underlying disease. Utilising machine learning methods, new insights into experimental variability and the logic (or patterns) of transcription factors have recently been discovered and utilised for the prediction and validation of completely new transcription factor interactions (Waardenberg et al. in press).

This project will involve extending these findings to develop and implement methods for the mining of transcription factor patterns in the context of heritable or rare disease data, using machine learning methods. The ultimate goal is to identify new mechanisms of disease.

Additional Information

Children’s Medical Research Institute (CMRI) is an award-winning state-of-the-art medical research facility, with over 100 full-time scientists dedicated to researching the genes and proteins important for health and human development. The CMRI is supported in part by its key fundraiser Jeans for Genes®. Our scientists are internationally recognised research leaders and foster excellence in postgraduate training. CMRI graduates are highly sought after nationally and internationally.  

CMRI is located at Westmead, a major hub for research and medicine in NSW, and is affiliated with the University of Sydney. Easy to access by public transport.  

We are looking for top quality students who can prove a dedicated interest and enthusiasm for scientific research. 

Candidates may apply for a CMRI PhD Research Award, which exceeds the Australian Postgraduate Awards and NHMRC scholarships in value. Visit the CMRI
website for more details.

Methodologies:

Bioinformatics
Visualisation
Statistics
Network Theory

Eligibility:
Honours entry: GPA on track for Honours Class I / IIA
PhD entry: Honours Class I
Experience with programming languages.

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Keywords

Bioinformatics, Statistics, Machine learning, gene regulation, disease

Opportunity ID

The opportunity ID for this research opportunity is: 2100