Volume 3 Issue 11, November 2021

As service and industrial robots enter our lives, new types of cybersecurity issues emerge that involve the manipulation of a robot’s behaviour. Now is the time to develop countermeasures.

Substantial advances have been made in the past decade in developing high-performance machine learning models for medical applications, but translating them into practical clinical decision-making processes remains challenging. This Perspective provides insights into a range of challenges specific to high-dimensional, multimodal medical imaging.

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.

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.

Optimization problems can be described in terms of a statistical physics framework. This offers the possibility to make use of ‘simulated annealing’, which is a procedure to search for a target solution similar to the gradual cooling of a condensed matter system to its ground state. The approach can now be sped up significantly by implementing a model of recurrent neural networks, in a new strategy called variational neural annealing.

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.

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.

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.

RNA structure profiling methods suffer from missing values in RNA structurome data. Inspired by a computer vision approach, Gong and colleagues develop a deep learning method that imputes missing RNA structure scores and increases the structural coverage of the transcriptome.