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The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

  • Milena Pavlović
  • Lonneke Scheffer
  • Geir Kjetil Sandve

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  • Radiofrequency pulses of different shapes can increase the efficiency of applications such as broadcasting or medical imaging, but finding the optimal shape for a specific use can be computationally costly. Shin and colleagues present a new method based on deep reinforcement learning to design radiofrequency pulses for use in MRI, which is demonstrated to cover different types of optimization goals for each application.

    • Dongmyung Shin
    • Younghoon Kim
    • Jongho Lee
  • The proliferation of molecular biology and bioinformatics tools necessary to generate huge quantities of immune receptor data has not been matched by frameworks that allow easy data analysis. The authors present immuneML, an open-source collaborative ecosystem for machine learning analysis of adaptive immune receptor repertoires.

    • Milena Pavlović
    • Lonneke Scheffer
    • Geir Kjetil Sandve
  • Identifying a chemical substance using mass spectrometry without knowing its structure is challenging. To help detect novel designer drugs from their mass spectra, Skinnider et al. describe a generative model that is biased towards creating potentially psychoactive molecules and thus helps identify potential candidates for a specific sample.

    • Michael A. Skinnider
    • Fei Wang
    • David S. Wishart
  • Providing patient specific predictions for drug responses is challenging as preclinical data across a large population is hard to collect. Sharifi-Noghabi and colleagues present a semi-supervised method to predict drug response from limited data that can generalize successfully to different tissue types.

    • Hossein Sharifi-Noghabi
    • Parsa Alamzadeh Harjandi
    • Martin Ester
  • Complex physical processes such as flow fields can be predicted using deep learning methods if good quality sensor data is available, but sparsely placed sensors and sensors that change their position present a problem. A new approach from Kai Fukami and colleagues based on Voronoi tessellation now allows to use data from an arbitrary number of moving sensors to reconstruct a global field.

    • Kai Fukami
    • Romit Maulik
    • Kunihiko Taira
  • Although the initial inspiration of neural networks came from biology, insights from physics have helped neural networks to become usable. New connections between physics and machine learning produce powerful computational methods.

  • Can the human brain cope with controlling an extra robotic arm or digit added to the body?

  • In the AlphaPilot Challenge, teams compete to fly autonomous drones through an obstacle course as fast as possible. The 2019 winning team MAVLab reflects on the challenge of beating human pilots.

    • C. De Wagter
    • F. Paredes-Vallés
    • G. de Croon
    Challenge Accepted
  • Very large neural network models such as GPT-3, which have many billions of parameters, are on the rise, but so far only big tech has the resources to train, deploy and study such models. This needs to change, say Stanford AI researchers, who call for an investment in academic collaborations to build and study large neural networks.

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.
Publishing online monthly from January 2019, Nature Machine Intelligence is interested in the best research from across the fields of artificial intelligence, machine learning and robotics. All editorial decisions are made by a team of full-time professional editors.
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