Bulk RNA sequencing in minor salivary glands of Brazilian patients with primary Sjögren’s syndrome (SS) and healthy volunteers.
Available in GEO: GSE154926 (Contributor(s) Chiorini JA, Mo Y, Pranzatelli TJ, Michael DG, Ji Y, Rocha EM)

Author: Agustin Alejandro Martinez Chibly

Data Source for this project: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE154926

Project Description: We will use supervised machine learning approach to predict sjogren's syndrome (ss) based on the transcriptional profile of salivary glands. We will test the performance of 6 different algorithms including logistic regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Desicion Tree Classifier (DTC), Gaussian Naive Bayes (NB), and Support Vector Machines (SVM), using a k-fold cross-validation approach. The algorithms with the better performance will be tested in the validation set to predict SS patients.