Lorin Crawford

The advancement of high‐throughput genomic assays has led to enormous growth in the availability of large‐scale biological datasets. Over the last two decades, these increasingly complex data have required statistical approaches that are more sophisticated than traditional linear models. Machine learning methodologies such as neural networks have yielded state‐of‐the‐art performance for prediction‐based tasks in many biomedical applications. However, a notable downside of these machine learning models is that they typically do not reveal how or why accurate predictions are made. In many areas of biomedicine, this “black box” property can be less than desirable—particularly when there is a need to perform in silico hypothesis testing about a biological system, in addition to justifying model findings for downstream decision‐making, such as determining the best next experiment or treatment strategy. Explainable and interpretable machine learning approaches have emerged to overcome this issue. While explainable methods attempt to derive post hoc understanding of what a model has learned, interpretable models are designed to inherently provide an intelligible definition of their parameters and architecture. Here, we review the model transparency spectrum moving from black box and explainable, to interpretable machine learning methodology. Motivated by applications in genomics, we provide background on the advances across this spectrum, detailing specific approaches in both supervised and unsupervised learning. Importantly, we focus on the promise of incorporating existing biological knowledge when constructing interpretable machine learning methods for biomedical applications. We then close with considerations and opportunities for new development in this space. This article is categorized under: Statistical Models > Nonlinear Models Applications of Computational Statistics > Genomics/Proteomics/Genetics Applications of Computational Statistics > Computational and Molecular Biology