Identifying critical transitions of complex diseases based on a single sample

摘要: Motivation: Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before serious deterioration a is challenging task, because system may show little apparent change or symptoms this critical transition during progression. By exploring rich interaction information provided by high-throughput data, dynamical network biomarker (DNB) can identify but requires multiple samples to reach correct for one individual, thereby restricting its clinical application. Results: In work, we have developed novel computational approach based on DNB theory and differential distributions between expressions non-DNB molecules, which detect reliably even from single sample taken compensating insufficient with datasets population studies. Our has been validated successful identification subjects individuals emergence acute lung injury, influenza, breast cancer.