Research articles

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

To train machine learning models for medical imaging, large amounts of training data are needed. Zhou and colleagues instead propose a method of weak supervision which uses the information of radiology reports to learn visual features without the need for expert labelling.

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.

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.

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.

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.

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.

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.

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.