Identifying critical transitions of complex diseases based on a single sample

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Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before the serious deterioration of a disease is a challenging task, because the state of the system may show little apparent change or symptoms before this critical transition during disease progression. By exploring the rich interaction information provided by high-throughput data, the dynamical network biomarker (DNB) can identify the pre-disease state, but this requires multiple samples to reach a correct diagnosis for one individual, thereby restricting its clinical application.


In this article, we have developed a novel computational approach based on the DNB theory and differential distributions between the expressions of DNB and non-DNB molecules, which can detect the pre-disease state reliably even from a single sample taken from one individual, by compensating insufficient samples with existing datasets from population studies. Our approach has been validated by the successful identification of pre-disease samples from subjects or individuals before the emergence of disease symptoms for acute lung injury, influenza and breast cancer.


Supplementary information:

Supplementary data are available at Bioinformatics online.

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