PMC:4718081 / 19214-20434 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4718081","sourcedb":"PMC","sourceid":"4718081","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4718081","text":"In this study, we propose a disease progression model termed as mini network balance model. The mini network balance model evaluates the disease progression in three ways. Firstly, the mini network balance map describes mini network imbalance visually. Secondly, the robustness of mini network is measured by mini network disruption probability which is a proxy for the disease progression. Thirdly, the integral variation of mini network is evaluated by the mini network disruption parameters U, K, and φ. The variation of mini network is usually complex. These three parameters decompose the complicated variation into simple variation. Firstly, parameter K is response to the consistency variation of biomarkers in mini network. Secondly, parameter φ represents the inconsistency variation of biomarkers in mini network. Parameter U is response to the total variation. With value of mini network integral parameters greater, the disease risk gets higher. The clinical relevance of mini network integral disruption parameters is that they can help enhance the accuracy and specificity of AD and MCI diagnosis. Especially, parameter U has the greatest contribution to the accuracy and specificity of the classification.","tracks":[]}