Opening the Black Box: Interpretable Machine Learning for Geneticists

Machine learning (ML) has emerged as a powerful tool for harnessing big biological data. The complex structure underlying ML models can potentially provide insights into the problems they are used to solve. Because of model complexity, their inner logic is not readily intelligible to a human, hence the common critique of ML models as black boxes. However, advances in the field of interpretable ML have made it possible to identify important patterns and features underlying an ML model using various strategies. These interpretation strategies have been applied in genetics and genomics to derive novel biological insights from ML models. This area of research is becoming increasingly important as more complex and difficult-to-interpret ML approaches (i.e., deep learning) are being adopted by biologists. 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. 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. the procedure taken to solve a problem/build a model. a model made up of a hierarchical series of true/false questions. a subset of ML algorithms inspired by the structure of the brain that can find complex, nonlinear patterns in data. an explanatory (i.e., independent) variable used to build a model. a type of ML interpretation that explains the overall relationship between the features and the label for all instances. a single example from which the model will learn from or be applied to. capable of being understood by a human. the variable to be predicted (i.e., the dependent variable). an ML interpretation that explains the relationship between the features and the label for one or a subset of instances. the set of patterns learned for a specific problem, where given input (i.e., instances and their features) the model will generate an output (i.e., prediction). a quantitative evaluation of the model’s ability to correctly predict labels. variables in an ML model whose values are optimized during training. a family of interpretation strategies that measure how changes in the input data impact model predictions or performance. a family of interpretation strategies that involve inspecting the structure and parameters in a trained model. a family of interpretation strategies that involve training an inherently interpretable model (e.g., a linear model) using the same data as a black box model to approximate the predictions of the black box model. the process of identifying the best parameters to make up a model: the learning part in ML.