Research articles

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  • Predicting binding of ligands to molecular targets is a key task in the development of new drugs. To improve the speed and accuracy of this prediction, Méndez–Lucio and colleagues developed DeepDock, a method that uses geometric deep learning to inform a statistical potential to find conformations of ligand–target pairs.

    • Oscar Méndez-Lucio
    • Mazen Ahmad
    • Jörg Kurt Wegner
  • 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
  • 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.

    • Mohamed Hibat-Allah
    • Estelle M. Inack
    • Juan Carrasquilla
  • Camera trapping is a widely adopted method for monitoring terrestrial mammals. However, a drawback is the amount of human annotation needed to keep pace with continuous data collection. The authors developed a hybrid system of machine learning and humans in the loop, which minimizes annotation load and improves efficiency.

    • Zhongqi Miao
    • Ziwei Liu
    • Wayne M. Getz
  • Combining generative models and reinforcement learning has become a promising direction for computational drug design, but it is challenging to train an efficient model that produces candidate molecules with high diversity. Jike Wang and colleagues present a method, using knowledge distillation, to condense a conditional transformer model to make it usable in reinforcement learning while still generating diverse molecules that optimize multiple molecular properties.

    • Jike Wang
    • Chang-Yu Hsieh
    • Tingjun Hou
  • A growing number of researchers are developing approaches to improve fairness in machine learning applications in areas such as healthcare, employment and social services, to avoid propagating and amplifying racial and other inequities. An empirical study explores the trade-off between increasing fairness and model accuracy across several social policy areas and finds that this trade-off is negligible in practice.

    • Kit T. Rodolfa
    • Hemank Lamba
    • Rayid Ghani
  • Turbulent optical distortions in the atmosphere limit the ability of optical technologies such as laser communication and long-distance environmental monitoring. A new method using adversarial networks learns to counter the physical processes underlying the turbulence so that complex optical scenes can be reconstructed.

    • Darui Jin
    • Ying Chen
    • Xiangzhi Bai
  • The use of sparse signals in spiking neural networks, modelled on biological neurons, offers in principle a highly efficient approach for artificial neural networks when implemented on neuromorphic hardware, but new training approaches are needed to improve performance. Using a new type of activity-regularizing surrogate gradient for backpropagation combined with recurrent networks of tunable and adaptive spiking neurons, state-of-the-art performance for spiking neural networks is demonstrated on benchmarks in the time domain.

    • Bojian Yin
    • Federico Corradi
    • Sander M. Bohté
  • T-cell immunity is driven by the interaction between peptides presented by major histocompatibility complexes (pMHCs) and T-cell receptors (TCRs). Only a small proportion of neoantigens elicit T-cell responses, and it is not clear which neoantigens are recognized by which TCRs. The authors develop a transfer learning model to predict TCR binding specificity to class-I pMHCs.

    • Tianshi Lu
    • Ze Zhang
    • Tao Wang
  • Spiking neural networks promise fast and energy-efficient information processing. The ‘time-to-first-spike’ coding scheme, where the time elapsed before a neuron’s first spike is utilized as the main variable, is a particularly efficient approach and Göltz and Kriener et al. demonstrate that error backpropagation, an essential ingredient for learning in neural networks, can be implemented in this scheme.

    • J. Göltz
    • L. Kriener
    • M. A. Petrovici
  • Incorporating prior knowledge in deep learning models can overcome the difficulties of supervised learning, including the need for large amounts of annotated data. An approach in this area called deep reasoning networks is applied to the complex task of mapping crystal structures from X-ray diffraction data for multi-element oxide structures, and identified 13 phases from 307 X-ray diffraction patterns in the previously unsolved Bi-Cu-V oxide system.

    • Di Chen
    • Yiwei Bai
    • Carla P. Gomes
  • The relationship between brain organization, connectivity and computation is not well understood. The authors construct neuromorphic artificial neural networks endowed with biological connection patterns derived from diffusion-weighted imaging. The neuromorphic networks are trained to perform a memory task, revealing an interaction between network structure and dynamics.

    • Laura E. Suárez
    • Blake A. Richards
    • Bratislav Misic
  • Radiomics has been used to discover imaging signatures that predict therapy response and outcomes, but clinical translation has been slow. Using machine learning methods, the authors report tumour subtypes that are applicable across major imaging modalities and three cancer types. The tumour subtypes have distinct radiological and molecular features, as well as survival outcomes after conventional therapies.

    • Jia Wu
    • Chao Li
    • Ruijiang Li