Volume 2 Issue 10, October 2020

Volume 2 Issue 10

Auditable autonomy on the road

Neural networks will have limited utility in high-risk environments unless their outputs can be reliably explained. In the cover image, Hasani et al. show how a compact controller inspired by the neural architecture of a roundworm may provide more robust and explainable outputs in a lane-following task. Also in this issue, Jiménez-Luna et al. review how explainable artificial intelligence approaches could aid in drug discovery.

See Lechner et al.

Image: Alexander Amini, Massachusetts Institute of Technology. Cover design: Karen Moore.

Editorial

  • Editorial |

    Robots can relieve humans of dangerous tasks. With the pandemic making physical contact potentially dangerous due to the risk of contagion, a new focus for robotic applications in healthcare has come into view.

Comment & Opinion

  • Comment |

    For machine learning developers, the use of prediction tools in real-world clinical settings can be a distant goal. Recently published guidelines for reporting clinical research that involves machine learning will help connect clinical and computer science communities, and realize the full potential of machine learning tools.

    • Bilal A. Mateen
    • , James Liley
    • , Alastair K. Denniston
    • , Chris C. Holmes
    •  & Sebastian J. Vollmer

News & Views

  • News & Views |

    Finding states of matter with properties that are just right is a main challenge from metallurgy to quantum computing. A data-driven optimization approach based on gaming strategies could help.

    • Eliska Greplova

Reviews

  • Perspective |

    Evidence syntheses produced from the scientific literature are important tools for policymakers. Producing such evidence syntheses can be highly time- and labour-consuming but machine learning models can help as already demonstrated in the health and medical sciences. This Perspective describes a machine learning-based framework specifically designed to support evidence syntheses in the area of agricultural research, for tackling the UN Sustainable Development Goal 2: zero hunger by 2030.

    • Jaron Porciello
    • , Maryia Ivanina
    • , Maidul Islam
    • , Stefan Einarson
    •  & Haym Hirsh
  • Perspective |

    Robots could play an important part in transforming healthcare to cope with the COVID-19 pandemic. This Perspective highlights how robotic technology integrated in a range of tasks in the surgical environment could help to ensure a continuation of medical services while reducing the risk of infection.

    • Ajmal Zemmar
    • , Andres M. Lozano
    •  & Bradley J. Nelson
  • 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

Research

  • Article |

    Advances in large-scale connectivity mapping of the brain require efficient computational tools to detect fine structures across large volumes of images, which poses challenges. The authors introduce a hybrid architecture that incorporates topological priors of neuronal structures with deep learning models to improve semantic segmentation of neuroanatomical image data.

    • Samik Banerjee
    • , Lucas Magee
    • , Dingkang Wang
    • , Xu Li
    • , Bing-Xing Huo
    • , Jaikishan Jayakumar
    • , Katherine Matho
    • , Meng-Kuan Lin
    • , Keerthi Ram
    • , Mohanasankar Sivaprakasam
    • , Josh Huang
    • , Yusu Wang
    •  & Partha P. Mitra
  • Article |

    Magnetic endoscopes have the potential to improve access, reduce patient discomfort and enhance safety. While navigation of magnetic endoscopes can be challenging for the operator, a new approach by Martin, Scaglioni and colleagues explores how to reduce this burden by offering different levels of autonomy in robotic colonoscopy.

    • James W. Martin
    • , Bruno Scaglioni
    • , Joseph C. Norton
    • , Venkataraman Subramanian
    • , Alberto Arezzo
    • , Keith L. Obstein
    •  & Pietro Valdastri
  • Article |

    Classifying cells from single-cell RNA sequences is challenging for cells where only limited data is available. Hu and colleagues show here that a clustering approach using transfer learning can use the variability of one dataset to cluster a smaller target dataset with high-quality results.

    • Jian Hu
    • , Xiangjie Li
    • , Gang Hu
    • , Yafei Lyu
    • , Katalin Susztak
    •  & Mingyao Li
  • Article |

    Recent advances have increased the dimensionality and complexity of immunological data. The authors developed a machine learning approach to incorporate prior immunological knowledge and applied it on clinical examples and a simulation study. The approach may be useful for high-dimensional datasets in clinical settings where the cohort size is limited.

    • Anthony Culos
    • , Amy S. Tsai
    • , Natalie Stanley
    • , Martin Becker
    • , Mohammad S. Ghaemi
    • , David R. McIlwain
    • , Ramin Fallahzadeh
    • , Athena Tanada
    • , Huda Nassar
    • , Camilo Espinosa
    • , Maria Xenochristou
    • , Edward Ganio
    • , Laura Peterson
    • , Xiaoyuan Han
    • , Ina A. Stelzer
    • , Kazuo Ando
    • , Dyani Gaudilliere
    • , Thanaphong Phongpreecha
    • , Ivana Marić
    • , Alan L. Chang
    • , Gary M. Shaw
    • , David K. Stevenson
    • , Sean Bendall
    • , Kara L. Davis
    • , Wendy Fantl
    • , Garry P. Nolan
    • , Trevor Hastie
    • , Robert Tibshirani
    • , Martin S. Angst
    • , Brice Gaudilliere
    •  & Nima Aghaeepour
  • Article |

    To infer a previously unknown molecular formula from mass spectrometry data is a challenging, yet neglected problem. Ludwig and colleagues present a network-based approach to ranking possible formulas.

    • Marcus Ludwig
    • , Louis-Félix Nothias
    • , Kai Dührkop
    • , Irina Koester
    • , Markus Fleischauer
    • , Martin A. Hoffmann
    • , Daniel Petras
    • , Fernando Vargas
    • , Mustafa Morsy
    • , Lihini Aluwihare
    • , Pieter C. Dorrestein
    •  & Sebastian Böcker
  • Article |

    Inspired by the brain of the roundworm Caenorhabditis elegans, the authors design a highly compact neural network controller directly from raw input pixels. Compared with larger networks, this compact controller demonstrates improved generalization, robustness and interpretability on a lane-keeping task.

    • Mathias Lechner
    • , Ramin Hasani
    • , Alexander Amini
    • , Thomas A. Henzinger
    • , Daniela Rus
    •  & Radu Grosu

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