Random Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin Generation.

ABSTRACT: Current methods for distinguishing acute coronary syndromes such as heart attack from stable coronary artery disease, based on the kinetics of thrombin formation, have been limited to evaluating sensitivity of well-established chemical species (e.g., thrombin) using simple quantifiers of their concentration profiles (e.g., maximum level of thrombin concentration, area under the thrombin concentration versus time curve). In order to get an improved classifier, we use a 34-protein factor clotting cascade model and convert the simulation data into a high-dimensional representation (about 19000 features) using a piecewise cubic polynomial fit. Then, we systematically find plausible assays to effectively gauge changes in acute coronary syndrome/coronary artery disease populations by introducing

SUBMITTER: Arumugam J 

PROVIDER: S-EPMC4865224 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature