Review Articles

Filter By:

Article Type
  • Both proteins and natural language are essentially based on a sequential code, but feature complex interactions at multiple scales, which can be useful when transferring machine learning models from one domain to another. In this Review, Ferruz and Höcker summarize recent advances in language models, such as transformers, and their application to protein design.

    • Noelia Ferruz
    • Birte Höcker
    Review Article
  • GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

    • Mohit Pandey
    • Michael Fernandez
    • Artem Cherkasov
    Review Article
  • 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.

    • Kenneth Atz
    • Francesca Grisoni
    • Gisbert Schneider
    Review Article
  • The development of extra fingers and arms is an exciting research area in robotics, human–machine interaction and wearable electronics. It is unclear, however, whether humans can adapt and learn to control extra limbs and integrate them into a new sensorimotor representation, without sacrificing their natural abilities. The authors review this topic and describe challenges in allocating neural resources for robotic body augmentation.

    • Giulia Dominijanni
    • Solaiman Shokur
    • Silvestro Micera
    Review Article
  • The popularity of deep learning is leading to new areas in biomedical applications. Wang and colleagues summarize in this Review the recent development and future directions of deep neural networks for superior image quality in the tomographic imaging field.

    • Ge Wang
    • Jong Chul Ye
    • Bruno De Man
    Review Article
  • Drug discovery has recently profited greatly from the use of deep learning models. However, these models can be notoriously hard to interpret. In this Review, Jiménez-Luna and colleagues summarize recent approaches to use explainable artificial intelligence techniques in drug discovery.

    • José Jiménez-Luna
    • Francesca Grisoni
    • Gisbert Schneider
    Review Article
  • Recent developments in machine learning have seen the merging of ensemble and deep learning techniques. The authors review advances in ensemble deep learning methods and their applications in bioinformatics, and discuss the challenges and opportunities going forward.

    • Yue Cao
    • Thomas Andrew Geddes
    • Pengyi Yang
    Review Article
  • This review covers the history of procedural content generation (PCG) approaches for video games, and how these approaches are now used to generate training data and environments for machine learning models. The authors then discuss how PCG may be crucial for training agents which generalise well.

    • Sebastian Risi
    • Julian Togelius
    Review Article
  • Predicting the properties of batteries, such as their state of charge and remaining lifetime, is crucial for improving battery manufacturing, usage and optimisation for energy storage. The authors discuss how machine learning methods and high-throughput experimentation provide a data-driven approach to this problem, and highlight challenges in building models which provide fast and accurate battery state predictions.

    • Man-Fai Ng
    • Jin Zhao
    • Zhi Wei Seh
    Review Article
  • This Review surveys machine learning techniques that are currently developed for a range of research topics in biological and artificial active matter and also discusses challenges and exciting opportunities. This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.

    • Frank Cichos
    • Kristian Gustavsson
    • Giovanni Volpe
    Review Article
  • Classical statistical analysis in many empirical sciences has lagged behind modern trends in analytics for large-scale datasets. The authors discuss the influence of more variables, larger sample sizes, open data sources for analysis and assessment, and ‘black box’ prediction methods on the empirical sciences, and provide examples from imaging neuroscience.

    • Danilo Bzdok
    • Thomas E. Nichols
    • Stephen M. Smith
    Review Article
  • Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.

    • Emre O. Neftci
    • Bruno B. Averbeck
    Review Article
  • Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

    • Kenneth O. Stanley
    • Jeff Clune
    • Risto Miikkulainen
    Review Article