Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
In the field of computational materials design, 3D microstructural datasets are crucial for understanding structure–performance relationships through physical modelling. However, 3D imaging can be slow and often has limited resolution compared to its 2D counterparts. In this issue, Steve Kench and Samuel Cooper propose a generative adversarial architecture, SliceGAN, which can use a single representative cross-sectional image to synthesize realistic 3D volumes. In an accompanying News & Views, Alejandro Franco discusses the technique and the potential to extend it to even further dimensional expansion.
Citizen scientists are empowered by mobile technology to collect data and crowdsource knowledge. Furthermore, automated machine learning tools allow non-experts in AI to analyse data. Ethical and regulatory questions arise, however, as data collection and AI technologies become enmeshed in people’s lives.
3D image reconstruction is important for the understanding of materials and their function in devices. A generative adversarial network architecture reconstructs 3D materials microstructures from 2D images.
Hyperspectral imaging can reveal important information without the need for staining. To extract information from this extensive data, however, new methods are needed that can interpret the spatial and spectral patterns present in the images.
The dynamical properties of a nonlinear system can be learned from its time-series data, but is it possible to predict what happens when the system is tuned far away from its training values?
Medical artificial intelligence and machine learning technologies marketed directly to consumers are on the rise. The authors argue that the regulatory landscape for such technologies should operate differently when a system is designed for personal use than when it is designed for clinicians and doctors.
Several technology companies offer platforms for users without coding experience to develop deep learning algorithms. This Analysis compares the performance of six ‘code-free deep learning’ platforms (from Amazon, Apple, Clarifai, Google, MedicMind and Microsoft) in creating medical image classification models.
A generative approach called SliceGAN is demonstrated that can construct complex three-dimensional (3D) images from representative two-dimensional (2D) image examples. This is a promising approach in particular for studying microstructured materials where acquiring good-quality 3D data is challenging; 3D datasets can be created with SliceGAN, making use of high-quality 2D imaging techniques that are widely available.
Imaging over a wide spectral profile allows for detailed chemical characterization, but processing this data is not trivial. A new neural network architecture can process hyperspectral information from a variety of imaging techniques effectively by using the popular U-net motive around another U-net processing spatial patterns.
Recurrent neural networks (RNNs) can learn to process temporal information, such as speech or movement. New work makes such approaches more powerful and flexible by describing theory and experiments demonstrating that RNNs can learn from a few examples to generalize and predict complex dynamics including chaotic behaviour.
A protein’s three-dimensional structure and properties are defined by its amino-acid sequence, but mapping protein sequence to protein function is a computationally highly intensive task. A new generative adversarial network approach learns from natural protein sequences and generates new, diverse protein sequence variations, which are experimentally tested.
While deep learning models have allowed the extraction of fingerprints from the structural description of molecules, they can miss information that is present in the molecular descriptors that chemists use. Shen and colleagues present a method to combine both sources of information into two-dimensional fingerprint maps, which can be used in a wide variety of pharmaceutical tasks to predict the properties of drugs.
The propagation of ultrashort pulses in optical fibres, of interest in scientific studies of nonlinear systems, depends sensitively on both the input pulse and the fibre characteristics and normally requires extensive numerical simulations. A new approach based on a recurrent neural network can predict complex nonlinear propagation in optical fibre, solely from the input pulse intensity profile, and helps to design experiments in pulse compression and ultra-broadband supercontinuum generation.
Cancers are complex diseases that are increasingly studied using a diverse set of omics data. At the same time, histological images show the interaction of cells, which is not visible with bulk omics methods. Binder and colleagues present a method to learn from both kinds of data, such that molecular markers can be associated with visible patterns in the tissue samples and be used for more accurate breast cancer diagnosis.