Selection of an Ideal Machine Learning Framework for Predicting Perturbation Effects on Network Topology of Bacterial KEGGPathways
Michael Robben, Mohammad Sadegh Nasr, Avishek Das, Manfred Huber, Justyn Wayne Jaworski, Jon A. Weidanz, Jacob M. Luber
The paper compares machine learning methods for re-annotating bacterial genes to improve KEGG pathway network accuracy.
bioRxiv · Jul 21, 2022 · PreprintBioinformatics