Researchers at Children’s Medical Research Institute (CMRI) have created an innovative tool to extract information from big data for improving the effectiveness of treatment options for children living with disease. 

A paper published in the prestigious Nature Communications details an exciting new tool that will improve the accuracy of large-scale metabolomics data that is collected over months and years. The work was led by PhD Student Taiyun Kim. Dr Pengyi Yang, Group Leader of Computational Systems Biology, and one of the co-authors, says the tool is important for one day allowing doctors to prescribe more precise and effective treatment option for diseases like cancer.

The Computational Systems Biology Group focuses on developing computational and statistical models to reconstruct molecular networks and model their regulations in differentiation and development. To translate computational predictions to biological findings, the group also focuses on experimentally validating hypotheses generated from computational models.

Through analysing large-scale omics data, the team hopes to identify biomarkers by comparing and contrasting how different treatments for different diseases affect different people. The aim is that doctors can then reference these biomarkers to avoid ineffective treatments for their patients. However, when data is collected on this scale, using liquid chromatography-mass spectrometry-based methods over months and even years, technical limitations result in unwanted variations. These unwanted variations can obscure the data and hinder biological discoveries.

In response to this, the Computational Systems Biology Group has developed a new data analysis tool that can correct the messy data. Their technique presents a significant improvement over existing methods of maintaining biological signals whilst removing unwanted variation for large-scale metabolomics studies.

“We developed a novel batch correction tool, enabling hierarchical Removal of Unwanted Variation (hRUV) in large-scale metabolomics data generated across months and years. This tool is essential for extracting biological signal from complex and noisy metabolomics data and will be a key enabler for precision medicine.”

Link to full publication: https://www.nature.com/article...