6 impactful applications of AI to the life sciences [new essay]

In 2013, the machine learning (ML) research community demonstrated the uncanny ability for deep neural networks trained with backpropagation on graphics processing units to solve complex computer vision tasks. The same year, I wrapped up my PhD in cancer research that investigated the genetic regulatory circuitry of cancer metastasis. Over the 6 years that followed, I've noticed more and more computer scientists (we call them bioinformaticians:) and software engineers move into the life sciences. This influx is both natural and extremely welcome. The life sciences have become increasingly quantitative disciplines thanks to high-throughput omics assays such as sequencing and high-content screening assays such as multi-spectral, time-series microscopy. If we are to achieve a step-change in experimental productivity and discovery in life sciences, I think it's uncontroversial to posit that we desperately need software-augmented workflows. This is the era of empirical computation (more on that here). But what life science problems should we tackle and what software approaches should we develop?