Asim Iqbal and Romesa Khan, University of Zurich and ETH Zurich

June issue out now

Nature Machine Intelligence is an online-only journal for research and perspectives from the fast-moving fields of artificial intelligence, machine learning and robotics. Launched in january 2019.

Latest Research

  • Article |

    High-throughput brain image registration methods that are independent of any pre-processing steps, and are robust under mild image transformations, could accelerate the study of region-specific changes in brain development. A deep learning-based method is therefore developed for automated registration through segmenting brain regions of interest with minimal human supervision.

    • Asim Iqbal
    • , Romesa Khan
    •  & Theofanis Karayannis
  • Article |

    Reducing the radiation dose for medical CT scans can provide a less invasive imaging method, but requires a method for reconstructing an image up to the image quality from a full-dose scan. In this article, Wang and colleagues show that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

    • Hongming Shan
    • , Atul Padole
    • , Fatemeh Homayounieh
    • , Uwe Kruger
    • , Ruhani Doda Khera
    • , Chayanin Nitiwarangkul
    • , Mannudeep K. Kalra
    •  & Ge Wang
  • Article |

    Diagnostic pathology currently requires substantial human expertise, often with high inter-observer variability. A whole-slide pathology method automates the prediction process and provides computer-aided diagnosis using artificial intelligence.

    • Zizhao Zhang
    • , Pingjun Chen
    • , Mason McGough
    • , Fuyong Xing
    • , Chunbao Wang
    • , Marilyn Bui
    • , Yuanpu Xie
    • , Manish Sapkota
    • , Lei Cui
    • , Jasreman Dhillon
    • , Nazeel Ahmad
    • , Farah K. Khalil
    • , Shohreh I. Dickinson
    • , Xiaoshuang Shi
    • , Fujun Liu
    • , Hai Su
    • , Jinzheng Cai
    •  & Lin Yang
  • Article |

    Deep neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

    • Julius Upmeier zu Belzen
    • , Thore Bürgel
    • , Stefan Holderbach
    • , Felix Bubeck
    • , Lukas Adam
    • , Catharina Gandor
    • , Marita Klein
    • , Jan Mathony
    • , Pauline Pfuderer
    • , Lukas Platz
    • , Moritz Przybilla
    • , Max Schwendemann
    • , Daniel Heid
    • , Mareike Daniela Hoffmann
    • , Michael Jendrusch
    • , Carolin Schmelas
    • , Max Waldhauer
    • , Irina Lehmann
    • , Dominik Niopek
    •  & Roland Eils
  • Perspective |

    There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

    • Cynthia Rudin
  • Analysis |

    Many functions of RNA strands that do not code for proteins are still to be deciphered. Methods to classify different groups of non-coding RNA increasingly use deep learning, but the landscape is diverse and methods need to be categorized and benchmarked to move forward. The authors take a close look at six state-of-the-art deep learning non-coding RNA classifiers and compare their performance and architecture.

    • Noorul Amin
    • , Annette McGrath
    •  & Yi-Ping Phoebe Chen

News & Comment

About the Journal

  • Nature Machine Intelligence aims to bring different fields together in the study, engineering and application of intelligent machines. We publish research on a large variety of topics in machine learning, robotics, cognitive science and a range of AI approaches. We also provide a platform for comments and reviews to discuss emerging inter-disciplinary themes as well as the significant impact that machine intelligence has on other fields in science and on society.

  • Nature Machine Intelligence publishes original research as Articles. We also publish a range of other content types including Reviews, Perspectives, Comments, Correspondences, News & Views and Feature articles.

  • Nature Machine Intelligence is run by a team of full-time editors. The Chief Editor is Liesbeth Venema who was previously a physics editor at Nature. Trenton Jerde started in March 2018, Yann Sweeney joined in July and Jacob Huth joined in November 2018, completing the team.

  • Contact information for editorial staff, submissions, the press office, institutional access and advertising at Nature Machine Intelligence