To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.

The Data Provenance Initiative audits over 1,800 text artificial intelligence (AI) datasets, analysing trends, permissions of use and global representation. It exposes frequent errors on several major data hosting sites and offers tools for transparent and informed use of AI training data.

Large language models can be queried to perform chain-of-thought reasoning on text descriptions of data or computational tools, which can enable flexible and autonomous workflows. Bran et al. developed ChemCrow, a GPT-4-based agent that has access to computational chemistry tools and a robotic chemistry platform, which can autonomously solve tasks for designing or synthesizing chemicals such as drugs or materials.

Using deep reinforcement learning, flexible skills and behaviours emerge in humanoid robots, as demonstrated in two recent reports.

Metagenome-assembled genomes (MAGs) provide insights into microbial dark matter, but contamination remains a concern for downstream analysis. Zou et al. develop a multi-modal deep language model that leverages microbial sequences to remove ‘unexpected’ contigs from MAGs. This approach is compatible with any contig binning tools and increases the number of high-quality bins.

Machine learning often includes secondary objectives, such as sparsity or robustness. To reach these objectives efficiently, the training of a neural network has been interpreted as the exploration of functionally invariant paths in the parameter space.

Walking efficiency declines in older adults. To address this challenge, Tricomi and colleagues present a pair of lightweight, soft robotic shorts that enhance walking efficiency for older adults by assisting leg mobility. This method improves energy efficiency on outdoor tracks while maintaining the users’ natural movement control.

In this Reusability Report, Heirman and Bittremieux evaluate MIST, a tool for annotating small-molecule mass spectrometry data, focusing on reproducibility and generalizability. They call for community efforts in benchmarking, transparency and data sharing to advance metabolomics research.

Predicting TCR–antigen–human leucocyte antigen binding opens the door to neoantigen identification. In this study, a physics-inspired sliding transformer (PISTE) system is used to guide the positioning of amino acid residues along the gradient field of their interactions, boosting binding prediction accuracy.

Stimulated emission depletion microscopy is a super-resolution imaging technique that utilizes point scanning in fluorescence microscopy. pySTED is developed to aid in the development and benchmarking of optical microscopy experiments, testing it in both synthetic and real settings.

New multi-modal AI methods fuse different biological data types that span multiple scales, offering promising clinical utility.

To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.

Medical research is one of the most impactful areas for machine learning applications, but access to large and diverse health datasets is needed for models to be useful. Winning trust from patients by demonstrating that data are handled securely and effectively is key.