Erika Cantor1, Rodrigo Salas2,3, Harvey Rosas4, Sandra Guauque-Olarte5. 1. Institute of Statistics, Universidad de Valparaíso, Valparaíso, Chile. erika.cantor@postgrado.uv.cl. 2. School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile. 3. Centro de Investigación y Desarrollo en Ingeniería en Salud, CINGS-UV, Universidad de Valparaíso, Valparaíso, Chile. 4. Institute of Statistics, Universidad de Valparaíso, Valparaíso, Chile. 5. Faculty of Dentistry, Universidad Cooperativa de Colombia, Envigado, Colombia.
Abstract
BACKGROUND: Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially implicated with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in comparison with patients having normal valves, using a knowledge-slanted random forest (RF). RESULTS: This study implemented a knowledge-slanted random forest (RF) using information extracted from a protein-protein interactions network to rank genes in order to modify their selection probability to draw the candidate split-variables. A total of 15,191 genes were assessed in 19 valves with CAVS (BAV, n = 10; TAV, n = 9) and 8 normal valves. The performance of the model was evaluated using accuracy, sensitivity, and specificity to discriminate cases with CAVS. A comparison with conventional RF was also performed. The performance of this proposed approach reported improved accuracy in comparison with conventional RF to classify cases separately with BAV and TAV (Slanted RF: 59.3% versus 40.7%). When patients with BAV and TAV were grouped against patients with normal valves, the addition of prior biological information was not relevant with an accuracy of 92.6%. CONCLUSION: The knowledge-slanted RF approach reflected prior biological knowledge, leading to better precision in distinguishing between cases with BAV, TAV, and normal valves. The results of this study suggest that the integration of biological knowledge can be useful during difficult classification tasks.
BACKGROUND:Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially implicated with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in comparison with patients having normal valves, using a knowledge-slanted random forest (RF). RESULTS: This study implemented a knowledge-slanted random forest (RF) using information extracted from a protein-protein interactions network to rank genes in order to modify their selection probability to draw the candidate split-variables. A total of 15,191 genes were assessed in 19 valves with CAVS (BAV, n = 10; TAV, n = 9) and 8 normal valves. The performance of the model was evaluated using accuracy, sensitivity, and specificity to discriminate cases with CAVS. A comparison with conventional RF was also performed. The performance of this proposed approach reported improved accuracy in comparison with conventional RF to classify cases separately with BAV and TAV (Slanted RF: 59.3% versus 40.7%). When patients with BAV and TAV were grouped against patients with normal valves, the addition of prior biological information was not relevant with an accuracy of 92.6%. CONCLUSION: The knowledge-slanted RF approach reflected prior biological knowledge, leading to better precision in distinguishing between cases with BAV, TAV, and normal valves. The results of this study suggest that the integration of biological knowledge can be useful during difficult classification tasks.
Authors: Ruben L J Osnabrugge; Darren Mylotte; Stuart J Head; Nicolas M Van Mieghem; Vuyisile T Nkomo; Corinne M LeReun; Ad J J C Bogers; Nicolo Piazza; A Pieter Kappetein Journal: J Am Coll Cardiol Date: 2013-05-30 Impact factor: 24.094