Enabling interpretable machine learning for biological data with reliability scores
by K. D. Ahlquist, Lauren A. Sugden, Sohini Ramachandran
Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased;... Читать дальше...