Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.

ABSTRACT:

Background

There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems.

Methods

In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this m

SUBMITTER: Liu J 

PROVIDER: S-EPMC9172070 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature