Prediction of prokaryotic origins of replication (ORI)
I have not seen the papers but I know the technique SVM. This is a machine learning approach its pretty good, but has been around this the 1980s. 

Its quite simple known ORIs are given with the associated genetic trait, e.g. GC skew, SVM will then regress the GC skew of a known ORI against the GC content of the genome, there will be a training, parameter optimization and test split in the data, typically 80:20:20. De novo data will then be feed into the trained algorithm, in the same way the test data was, and a prediction will be generated. There are weaknesses, the authors have used only one ML technique, typically numerous will be used e.g. SVM, random forest, lasso regression etc... In addition, the prediction accuracy for GC is low (75%), in addition 500 genomes isn't great for ML, you need 1000s.