Alessandro Giuliani

TL;DR: Recurrence quantification analysis (RQA) as discussed by the authors detects state changes in drifting dynamical systems without necessitating any a priori constraining mathematical assumptions and has been shown to be ideal for analyzing complex biological systems whose equations are unknown and whose dynamics are characteristically non-linear and non-stationary.

TL;DR: The proposed technique, cross-recurrence quantification, demonstrates the ability to extract signals up to a very low signal-to-noise-ratio and to allow an immediate appreciation of their degree of periodicity.

TL;DR: Early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease.

TL;DR: A tight immunosurveillance coupled with a functional p53 gene response is consistent with the better prognosis of MSI cancers, and the DNA repair gene expression profile of gastric cancer with MSI is of relevance for therapy response.