Jonathan M. Garibaldi

TL;DR: In this article, the authors assessed whether machine-learning can improve cardiovascular risk prediction and found that machine learning offers an opportunity to improve accuracy by exploiting complex interactions between risk factors, which can increase the number of patients who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

TL;DR: This study identifies the presence of variations in global levels of histone marks in different grades, morphologic types, and phenotype classes of invasive breast cancer and shows that these differences have clinical significance.

TL;DR: This work used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus and revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90° gravity stimulus.

TL;DR: To address the practical problems of routine umbilical cord blood sampling, to determine the ranges for pH, pCO2 and base deficit and to examine the relationships of these parameters between cord vessels.

TL;DR: This paper gives a comprehensive review of the application of metaheuristics to optimization problems in systems biology, mainly focusing on the parameter estimation problem (also called the inverse problem or model calibration).