AbstractTolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of a central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model.Using public transcriptomic data, we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at systems level were include to describe the perturbome elements.We were able to select and describe 46 core response genes associated to multiple perturbations in Pseudomonas aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels and topological features in molecular networks revealed biological functions of biosynthesis, binding and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including normalization analysis, data partitioning, classification approaches and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.
Machine learning approach for the search of resonances with topological features at the Large Hadron Collider