New Funding to Support Investigations of CFS/ME

Thursday 30 June 2016

Machine learning will be critical to this project, particularly to an integrated model of pathology and genetic marker networks

New Funding to Support Investigations of CFS/ME Genetics and Questions in Diagnostic Pathology via Machine Learning

The Pattern Recognition and Pathology Group (JCSMR Department of Genome Sciences) has attracted funding from the Commonwealth Department of Health (Quality Use of Pathology Programme) to investigate predictors influencing the quality of diagnostic pathology results, and provide advice on how to detect potential problems early. The Pattern Recognition group has previously conducted research into pathology results, using machine learning to look at higher dimension data networks to assist clinical decision-making in terms of disease diagnosis and monitoring.

The project is in collaboration with the Quality Assurance Programme (QAP) of the Royal College of Pathologists (Tony Badrick), NSW Health Pathology (Gus Koerbin) and NCEPH (Alice Richardson).

The application of pathology data to biomarker research for chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) has received a boost through funding from ME Research UK to explore the genetics of this disease using a pooling/bootstrap sequencing strategy. This project is in collaboration with Claudio Mastronardi and Mauricio Arcos-Burgos (Genomics and Predictive Medicine Group, JCSMR), who pioneered the pooling/bootstrap method, and the CFS Discovery Clinic (Donvale, Victoria).

 

 

DETAILS:

1) Development of Predictive Metrics that Allow Early Detection of Poor Laboratory Performance Via Machine-Learning Algorithms to Improve Patient Outcomes and Save Health Resources - Pilot Study and Systematic Review ($106,000 – Commonwealth Department of Health, Quality Use of Pathology Programme. Co-investigators: Tony Badrick (RCPA-QAP), Alice Richardson (ANU) and Gus Koerbin (NSW Health Pathology).

2) Two Dimensional (2D) Sequencing and Machine Learning to Maximise Genetic Marker Detection in a Clinically Well-Defined ME Cohort for Enhanced Diagnosis (49,750 GBP ~ $105,000 AUD - ME Research UK. Co-investigators: Mauricio Arcos-Burgos, Claudio Mastronardi, and in collaboration with CFS Discovery, Donvale Victoria).

Updated:  17 December 2017/Responsible Officer:  Director, JCSMR/Page Contact:  Web Manager