Research articles

Filter By:

  • High-throughput single-cell sequencing data can provide valuable biological insights but are computationally challenging to analyse due to the dimensionality of the data and batch-specific biases. Kopp and colleagues have developed a variational auto-encoder-based method using a novel loss function for simultaneous batch correction and dimensionality reduction.

    • Wolfgang Kopp
    • Altuna Akalin
    • Uwe Ohler
    Article Open Access
  • Tropical diseases, such as malaria, can develop resistance to specific drugs. Godinez and colleagues present here a generative design approach to find new anti-malarial drugs to circumvent this resistance.

    • William J. Godinez
    • Eric J. Ma
    • W. Armand Guiguemde
  • The Large Hadron Collider produces 40 million collision events per second, most of which need to be discarded by a real-time filtering system. Unsupervised deep learning algorithms are developed and deployed on custom electronics to search for rare events indicating new physics, rather than for specific events led by theory.

    • Ekaterina Govorkova
    • Ema Puljak
    • Zhenbin Wu
  • High-fidelity haptic sensors with three-dimensional sensing surfaces are needed to advance dexterous robotic manipulation. The authors develop a sensor design that offers accurate force sensation across a three-dimensional surface while being robust, low-cost and easy to fabricate.

    • Huanbo Sun
    • Katherine J. Kuchenbecker
    • Georg Martius
    Article Open Access
  • The combination of object recognition and viewpoint estimation is essential for visual understanding. However, convolutional neural networks often fail to generalize to object category–viewpoint combinations that were not seen during training. The authors investigate the impact of data diversity and architectural choices on the capability of generalizing to out-of-distribution combinations.

    • Spandan Madan
    • Timothy Henry
    • Xavier Boix
  • Controllers for robotic locomotion patterns deal with complex interactions and need to be carefully designed or extensively trained. Thor and Manoonpong present a modular approach for neural pattern generators that allows incremental and fast learning.

    • Mathias Thor
    • Poramate Manoonpong
  • The investigation of single-cell epigenomics with technologies such as single-cell chromatin accessibility sequencing (scCAS) presents an opportunity to expand the understanding of gene regulation at the cellular level. The authors develop a probabilistic generative model to better characterize cell heterogeneity and accurately annotate the cell type of scCAS data.

    • Xiaoyang Chen
    • Shengquan Chen
    • Rui Jiang
  • Molecules are often represented as topological graphs while their true three-dimensional geometry contains a lot of valuable information. Xiaomin Fang and colleagues present a self-supervised molecule representation method that uses this geometric data in graph neural networks to predict a range of molecular properties.

    • Xiaomin Fang
    • Lihang Liu
    • Haifeng Wang
    Article Open Access
  • Piezoresistors can be used in strain sensors for soft machines, but the traditional design process relies on intuition and human ingenuity alone. Haitao Yang and colleagues present a method built on genetic algorithms and other machine learning methods to design and fabricate strain sensors with improved capabilities.

    • Haitao Yang
    • Jiali Li
    • Po-Yen Chen
  • Neural networks have become a useful approach for predicting biological function from large-scale DNA and protein sequence data; however, researchers are often unable to understand which features in an input sequence are important for a given model, making it difficult to explain predictions in terms of known biology. The authors introduce scrambler networks, a feature attribution method tailor-made for discrete sequence inputs.

    • Johannes Linder
    • Alyssa La Fleur
    • Georg Seelig
  • In artificial neural networks, a typical neuron generally performs a simple summation of inputs. Using computational and electrophysiological data, the authors show that a single neuron predicts its future activity. Neurons that predict their own future responses are a potential mechanism for learning in the brain and neural networks.

    • Artur Luczak
    • Bruce L. McNaughton
    • Yoshimasa Kubo
    Article Open Access
  • Routine eye clinic imaging could help screen patients with cardiovascular risk as studies indicate strong associations between biomarkers in the retina and the heart. This potential is supported by a multimodal study, employing a deep learning model, that can infer cardiac functional indices based on retinal images and demographic data.

    • Andres Diaz-Pinto
    • Nishant Ravikumar
    • Alejandro F. Frangi
  • Reinforcement learning has shown remarkable success in areas such as game-playing and protein folding, but it has not been extensively explored in modelling cell behaviour. The authors develop an approach that uses deep reinforcement learning to uncover collective cell behaviours and the underlying mechanism of cell migration from 3D time-lapse images of tissues.

    • Zi Wang
    • Yichi Xu
    • Zhirong Bao
  • Zeroth-order optimization is used on problems where no explicit gradient function is accessible, but single points can be queried. Hoffman et al. present here a molecular design method that uses zeroth-order optimization to deal with the discreteness of molecule sequences and to incorporate external guidance from property evaluations and design constraints.

    • Samuel C. Hoffman
    • Vijil Chenthamarakshan
    • Payel Das
  • The black-box nature of neural networks is a concern for high-stakes medical applications in which decisions must be based on medically relevant features. The authors develop an interpretable machine learning-based framework that aims to follow the reasoning processes of radiologists in providing predictions for cancer diagnosis in mammography.

    • Alina Jade Barnett
    • Fides Regina Schwartz
    • Cynthia Rudin
  • The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

    • Xiang Bai
    • Hanchen Wang
    • Tian Xia
    Article Open Access
  • To train deep learning methods to segment very small subcellular structures, the training data have to be labelled by experts as the optical effects at such a small scale and the narrow depth of focus make it difficult to identify individual structures. Sekh et al. use a physics-based simulation approach to train neural networks to automatically segment subcellular structures despite the optical artefacts.

    • Arif Ahmed Sekh
    • Ida S. Opstad
    • Dilip K. Prasad
    Article Open Access
  • Predicting the function of proteins in newly sequenced organisms is a challenging problem. Mateo Torres et al. present here a method to transfer the functional relations from known organisms and improve the prediction using network diffusion.

    • Mateo Torres
    • Haixuan Yang
    • Alberto Paccanaro
  • To improve desired properties of drugs or other molecules, deep learning can be used to guide the optimization process. Chen et al. present a method that optimizes molecules one fragment at a time and requires fewer parameters and training data while still improving optimization performance.

    • Ziqi Chen
    • Martin Renqiang Min
    • Xia Ning