Opening the Black Box: Interpretable Machine Learning for Geneticists.
TL
TL;DR
The importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied are discussed, and challenges and promising future directions for interpretableML in genetics and genomics are identified.
Abstract
Because of its ability to find complex patterns in high dimensional and heterogeneous data, machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights. Here, we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.