Open AccessPersonalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Author(s) -
Ratna Dutta,
Karim Zouaoui Boudjeltia,
Christos Kotsalos,
Alexandre Fontaine Rousseau,
Daniel Ribeiro de Sousa,
Jean-Marc Desmet,
Alain Van Meerhaeghe,
Antonietta Mira,
Bastien Chopard
Publication year - 2022
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009910
Subject(s) - discriminative model , bayes' theorem , approximate bayesian computation , statistic , bayesian probability , computer science , test statistic , disease , personalized medicine , artificial intelligence , machine learning , statistical hypothesis testing , medicine , bioinformatics , pathology , statistics , mathematics , biology , inference
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.