A survey on common biostatistics tools in neuroscience: Machine learning and Bayesian modeling

Abstract

Machine learning was characterized by building models and finding correlations between data features, while logistic regression, decision trees, support vector machines (SVM), random forest (RF) and neural networks were recognized as common ML approaches. Bayesian modeling model uncertainty, which can estimate the features from the dataset directly instead of from sampling distribution. Their roles were extremely useful for the detection and progression for diseases in neuroscience. This review summarize different approaches in various diseases, hoping to introduce the potential roles of biostatistics tools in neuroscience.