化学与应用化学工程学报

Probabilistic Universal Model Approximator (PUMA): A Novel Algorithm for Visualizing Classification Models

Statement of the Problem: Analysis of data is the most the most challenging step in metabolomics experiments. In part, this is related to the enormous amount of data generated by metabolomics analytical methods. Chemometrics, especially principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) have been the most commonly used methods for analyzing metabolomics data. Recently, the increase in complexity of metabolomics data sets further increased the reliance on more sophisticated supervised classification algorithms (e.g. support vector machine (SVM) and random forest (RF)) for analyzing metabolomics data.