Kaileigh, along with former lab postdoc Lauren Sugden, led a new paper out in PLoS Computational Biology! The study, entitled Enabling intepretable machine learning for biological data with reliability scores, develops reliability scores for machine learning classifiers that help researchers evaluate how trustworthy the prediction of a SWIF(r) classification model is when classifying a specific instance of data. We illustrated multiple applications to biological problems and show that SWIF(r) reliability scores are helpful in situations where the data used to train the machine learning model fails to represent the testing data in some way. Congrats to all the authors on this exciting collaboration and publication!