Reviews & Analysis

Cooperation is crucial for human prosperity, and population structure fosters it through pairwise interactions and coordinated behavior in larger groups. A recent study explores the evolution of behavioral strategies in higher-order population structures, including pairwise and multi-way interactions to reveal that higher-order interactions promote cooperation across networks, especially when they are formed by conjoined communities.

SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.

Approaches are needed to accelerate the discovery of transition metal complexes (TMCs), which is challenging owing to their vast chemical space. A large dataset of diverse ligands is now introduced and leveraged in a multiobjective genetic algorithm that enables the efficient optimization of TMCs in chemical spaces containing billions of them.

While there is a clear opportunity for digital twins to bring value in mechanical and aerospace engineering, they must be considered as an asset in their own right so that their full potential can be realized.

The application of digital twins in industry has become increasingly common, but not without important challenges to be addressed by the research community.

Although digital twins first originated as models of physical systems, they are rapidly being applied to social systems, such as cities. This Perspective discusses the development and use of digital twins for urban planning.

The digital twin concept, while initially formulated and developed in industry and engineering, has compelling potential applications in medicine. There are, however, major challenges that need to be overcome to fully embrace digital twin technology in the medical context.

A recent study introduces a machine learning approach to investigate the effects of mutations on protein sensors commonly employed in fluorescence microscopy, thus enabling the discovery of high-performance sensors.

A neural network-based method for advancing orbital-free density functional theory (OFDFT) is developed, which reaches DFT accuracy and maintains lower cost complexity.

Determining what guest can effectively bind in a host, or the reverse, is a central challenge in chemistry. To address this, an electron-density-based transformer method of generating and optimizing host–guest binders is proposed, applied to two different host systems and validated by experiment.

We present SCORPION, a computational tool to model gene regulatory networks based on single-cell transcriptomic data and prior knowledge of gene regulation. SCORPION networks can be modeled for specific cell types in individual samples, and are therefore suitable for conducting comparisons between experimental groups.

As computation is increasingly integrated into drug research and development, this Perspective analyzes company business models, funding and deals to provide unique insights into risks and opportunities in this quickly maturing industry, which aims to expedite the creation of life-saving therapeutics.

One of the greatest limitations of deep neural networks is the difficulty of interpreting what they learn from the data. Deep distilling addresses this problem by extracting human-comprehensible and executable code from observations.

A method for correcting errors in the spatial-genetic reconstruction of DNA microscopy is proposed, leading to more accurate results and potential to resolve new biology.

Language models offer promises in encoding quantum correlations and learning complex quantum states. This Perspective discusses the advantages of employing language models in quantum simulation, explores recent model developments, and offers insights into opportunities for realizing scalable and accurate quantum simulation.

The laws of physics, formulated in a compact form, are elusive for complex dynamic phenomena. However, it is now shown that, using artificial intelligence constrained by the physical Onsager principle, a custom thermodynamic description of a complex system can be constructed from the observation of its dynamical behavior.

A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.

EmerGNN is a flow-based graph neural network (GNN) approach that advances on conventional methodologies for predicting drug–drug interactions in emerging drugs by effectively leveraging biomedical networks.

Transformer methods are revolutionizing how computers process human language. Exploiting the structural similarity between human lives, seen as sequences of events, and natural-language sentences, a transformer method — dubbed life2vec — has been used to create rich vector representations of human lives, from which accurate predictions can be made.

It is difficult to identify stable surface reconstructions of complex materials. Now a Monte Carlo sampling strategy is coupled with a machine learning interatomic potential that is iteratively improved via active learning during the search.