Volume 2 Issue 7, July 2020

Artificial intelligence tools can help save lives in a pandemic. However, the need to implement technological solutions rapidly raises challenging ethical issues. We need new approaches for ethics with urgency, to ensure AI can be safely and beneficially used in the COVID-19 response and beyond.

An important task in system biology is to understand cellular processes through the lens of gene sets and their expression patterns. Machine learning can help, but genes form complex interaction networks, and levarging this information in machine learning applications requires a sophisticated data representation.

Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.

A goal of biology is to identify the molecular mechanisms that control differential gene expression. Tasaki et al. have developed a framework that integrates genomic data into a deep learning model of transcriptome regulations to predict multiple transcriptional effects in tissue- and person-specific transcriptomes.

Gene sets can provide valuable information for gaining insight into disease mechanisms and cellular functions. In this paper, the authors use a Gaussian approach to represent gene sets and gene networks in a low-dimensional space, allowing for accurate prediction and decreased computational complexity.

Currently available quantum hardware is limited by noise, so practical implementations often involve a combination with classical approaches. Sels et al. identify a promising application for such a quantum–classic hybrid approach, namely inferring molecular structure from NMR spectra, by employing a range of machine learning tools in combination with a quantum simulator.

Machine learning has become popular in solving complex optical problems such as recovering the input phase and amplitude for a specific pattern or image measured through a scattering medium. In a more challenging application, Rahmani et al. consider the problem of also producing desired outputs for such a nonlinear system when only some intensity-only measurements of example outputs are available. They develop a neural network approach that can ensure the transmission of images through a highly nonlinear system—a multimode fibre—with a 90% fidelity.

Deep learning methods can be a powerful part of digital pathology workflows, provided well-annotated training datasets are available. Tolkach and colleagues develop a deep learning model to recognize and grade prostate cancer, based on a convolution neural network and a dataset with high-quality labels at gland-level precision.