Articles in 2022

Mutation rates are crucial for genetic and evolutionary analyses. Fang et al. present a generalizable deep learning method to build fine-scale mutation rate maps with DNA sequences as input, which can benefit analyses reliant on mutation rates.

Artificial intelligence systems are used for an increasing range of intellectual tasks, but can they invent, or will they be able to do so soon? A recent series of patent applications for two inventions that are claimed to have been made by an artificial intelligence program are bringing these questions to the fore.

A challenge for any machine learning system is to continually adapt to new data. While methods to address this issue are developed, their performance is hard to compare. A new framework to facilitate benchmarking divides approaches into three categories, defined by whether models need to adapt to new tasks, domains or classes.

Recent developments in deep learning have allowed for a leap in computational analysis of epigenomic data, but a fair comparison of different architectures is challenging. Toneyan et al. use GOPHER, their new framework for model evaluation and comparison, to perform a comprehensive analysis, exploring modelling choices of deep learning for epigenomic profiles.

Generative AI that transforms candidate molecules into potent drugs could pave the way towards better, faster drug design. Chan and co-workers developed a self-contrastive learning framework that helps AI models overcome data biases and provide explainable insights for augmented interactive design.

The lack of generalizability and reproducibility of machine learning models in medical applications is increasingly recognized as a substantial barrier to implementing such approaches in real-world clinical settings. Highlighting this issue, Jie Cao et al. aim to reproduce a recent acute kidney injury prediction model, and find persistent discrepancies in model performance in different subgroups.

A promising area for deep learning is in modelling complex physical processes described by partial differential equations (PDEs), which is computationally expensive for conventional approaches. An operator learning approach called DeepONet was recently introduced to tackle PDE-related problems, and in new work, this approach is extended with transfer learning, which transfers knowledge obtained from learning to perform one task to a related but different task.

A design contest for object detection with deep learning on embedded small devices leads to winning hardware–software co-design approaches.

AI promises to bring many benefits to healthcare and research, but mistrust has built up owing to many instances of harm to under-represented communities. To amend this, participatory approaches can directly involve communities in AI research that will impact them. An important element of such approaches is ensuring that communities can take control over their own data and how they are shared.

Predicting the properties of a molecule from its structure with high accuracy is a crucial problem in digital drug design. Instead of sequence features, Zeng and colleagues use an image representation of a large collection of bioactive molecules to pretrain a model that can be fine-tuned on specific property prediction tasks.

Predicting disease progression is an important medical problem, but it can be challenging for end-to-end machine learning approaches. Han and colleagues demonstrate that generative models can work together with medical experts to jointly predict the progression of a disease, osteoarthritis.

The use of decision-support systems based on artificial intelligence approaches in antimicrobial prescribing raises important moral questions. Adopting ethical frameworks alongside such systems can aid the consideration of infection-specific complexities and support moral decision-making to tackle antimicrobial resistance.

Indigenous peoples are under-represented in genomic datasets, which can lead to limited accuracy and utility of machine learning models in precision health. While open data sharing undermines rights of Indigenous communities to govern data decisions, federated learning may facilitate secure and community-consented data sharing.

Finding good benchmarks is an important and pervasive problem in machine learning for healthcare. This Perspective highlights key aspects that require scrutiny in the whole process of benchmark generation and use, including problem formulation, creation of datasets, development of a suite of machine learning models and evaluation of these models.

The metaverse is gaining prominence in industry, academia and social media. Wang and colleagues envision a medical technology and AI ecosystem, and present this perspective on the future of healthcare in the metaverse.

Physical dynamical processes can be modelled with differential equations that may be solved with numerical approaches, but this is computationally costly as the processes grow in complexity. In a new approach, dynamical processes are modelled with closed-form continuous-depth artificial neural networks. Improved efficiency in training and inference is demonstrated on various sequence modelling tasks including human action recognition and steering in autonomous driving.

Computational methods are important for interpreting missense variants in genetic studies and clinical testing. Zhang and colleagues develop a method based on graph attention neural networks to predict pathogenic missense variants. The method pools information from functionally correlated positions and can improve the interpretation of missense variants.

To enable electrification of cities, thereby decarbonizing energy grids, it is essential to predict electricity consumption with high spatio-temporal accuracy. To reduce the need for large amounts of training data, an active deep learning approach is developed to make accurate forecasts of electric load profiles at the scale of single buildings.