Gaining A Diagnostic Edge In Single-Sample Individuals

A computational framework that converts node data into edge data could help researchers identify causal networks in single-sample patients.

AsianScientist (May 22, 2015) – Scientists have devised a network approach to identifying biomarkers from a single patient sample, a computational framework called EdgeBiomarker. Their research has been published in the Journal of Molecular Cell Biology.

Complex diseases typically result from the failure of the relevant system rather than individual molecules or components. Therefore, networks or edges among molecules are considered to be a stable and reliable form for characterizing complex diseases or phenotypes as biomarkers.

Generally, an edge in a molecular network is represented by its correlation coefficient, a measurement which requires several samples to calculate. But for many biomedical applications, there is only one sample for one individual. Hence, it is difficult to diagnose an individual with a single sample by using correlation coefficient.

To address this issue, Professor Chen Luonan and his group from the Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences developed a new method for diagnosing single-sample individual by using the correlation information of molecular pairs, i.e. edge biomarkers.

Using this method, they designed a data transformation that turns ‘node data’ of molecular expression into ‘edge data’ which capture the information of correlation for each sample. Feature selection methods are then applied to edge data to extract the features with best classification accuracy as edge biomarkers.

The researchers applied the method on lung adenocarcinoma dataset from The Cancer Genome Atlas database and showed the advantages of edge biomarkers. They achieved better cross-validation accuracy of diagnosis than molecule or node biomarkers.

Edge biomarkers were found to be significantly enriched in relevant biological functions or pathways, implying that the association changes in a network—rather than expression changes in individual molecules—tend to be causally related to cancer development. The method identifies edge biomarkers even with non-differential molecules, which are generally missed by traditional approaches.

These findings provide a powerful tool for scientists working in big-data, personalized and precision medicine.

The article can be found at: Zhang et al. (2015) Diagnosing Phenotypes Of Single-sample Individuals By Edge Biomarkers.


Source: Chinese Academy of Sciences.
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