Volume 3 Issue 12, December 2021

The provisioning of information about product attributes in e-commerce environments is today left entirely to owners of online platforms. Product transparency in online stores can be increased by client-side enrichment of retailer Web pages.

Newly sequenced organisms present a challenge for protein function prediction, as they lack experimental characterisation. A network-propagation approach that integrates functional network relationships with protein annotations, transferred from well-studied organisms, produces a more complete picture of the possible protein functions.

Digitally recreating the likeness of a person used to be a costly and complex process. Through the use of generative models, AI-generated characters can now be made with relative ease. Pataranutaporn et al. discuss in this Perspective how this technology can be used for positive applications in education and well-being.

Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

Predicting binding of ligands to molecular targets is a key task in the development of new drugs. To improve the speed and accuracy of this prediction, Méndez–Lucio and colleagues developed DeepDock, a method that uses geometric deep learning to inform a statistical potential to find conformations of ligand–target pairs.

To improve desired properties of drugs or other molecules, deep learning can be used to guide the optimization process. Chen et al. present a method that optimizes molecules one fragment at a time and requires fewer parameters and training data while still improving optimization performance.

Predicting the function of proteins in newly sequenced organisms is a challenging problem. Mateo Torres et al. present here a method to transfer the functional relations from known organisms and improve the prediction using network diffusion.

The black-box nature of neural networks is a concern for high-stakes medical applications in which decisions must be based on medically relevant features. The authors develop an interpretable machine learning-based framework that aims to follow the reasoning processes of radiologists in providing predictions for cancer diagnosis in mammography.

To train deep learning methods to segment very small subcellular structures, the training data have to be labelled by experts as the optical effects at such a small scale and the narrow depth of focus make it difficult to identify individual structures. Sekh et al. use a physics-based simulation approach to train neural networks to automatically segment subcellular structures despite the optical artefacts.

The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.