Supervised learning methods in modeling of CD4+ T cell heterogeneity

BioData Mining
Pinyi LuJosep Bassaganya-Riera

Abstract

Modeling of the immune system - a highly non-linear and complex system - requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales. This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and L...Continue Reading

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Citations

Feb 26, 2016·Frontiers in Nutrition·Meghna VermaJosep Bassaganya-Riera
Apr 26, 2017·Stroke; a Journal of Cerebral Circulation·Vida AbediRamin Zand
Feb 16, 2021·Frontiers in Nutrition·Yogesh SinghFlorian Lang

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Software Mentioned

IL17
Random
randomForest
RF
SBML
MSM
R
ENISI
R Package
ENISI MSM

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