  High-throughput technologies such as next generation sequencing allow
biologists to observe cell function with unprecedented resolution, but the
resulting datasets are too large and complicated for humans to understand
without the aid of advanced statistical methods. Machine learning (ML)
algorithms, which are designed to automatically find patterns in data, are well
suited to this task. Yet these models are often so complex as to be opaque,
leaving researchers with few clues about underlying mechanisms. Interpretable
machine learning (iML) is a burgeoning subdiscipline of computational
statistics devoted to making the predictions of ML models more intelligible to
end users. This article is a gentle and critical introduction to iML, with an
emphasis on genomic applications. I define relevant concepts, motivate leading
methodologies, and provide a simple typology of existing approaches. I survey
recent examples of iML in genomics, demonstrating how such techniques are
increasingly integrated into research workflows. I argue that iML solutions are
required to realize the promise of precision medicine. However, several open
challenges remain. I examine the limitations of current state of the art tools
and propose a number of directions for future research. While the horizon for
iML in genomics is wide and bright, continued progress requires close
collaboration across disciplines.
