ArticleFrontiers in physiology2018
Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation With High Performance Computing.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.PLoS computational biology · 2022Article
- Shear induced diffusion of platelets revisited.Frontiers in physiology · 2022Article
- Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching.Royal Society open science · 2021Article
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6 authors.
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Abstract
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. Recent studies show the existing clinical tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions. Further they are also incapable to consider inter-individual variability. A physical description of platelets deposition was introduced recently in Chopard et al. (2017), by integrating fundamental understandings of how platelets interact in a numerical model, parameterized by five parameters. These parameters specify the deposition process and are relevant for a biomedical understanding of the phenomena. One of the main intuition is that these parameters are precisely the information needed for a pathological test identifying CVD captured and that they capture the inter-individual variability. Following this intuition, here we devise a Bayesian inferential scheme for estimation of these parameters, using experimental observations, at different time intervals, on the average size of the aggregation clusters, their number per mm
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