Research articles

An efficient method for parallelizing the contraction of tensor networks pushes the boundaries for the classical simulation of quantum computation, and aids the development of quantum algorithms and hardware.

The study introduces the design and implementation of a parallel computational framework, called HiCOPS, for efficient acceleration of large-scale database peptide search workloads on supercomputers.

Optical computing promises high-speed computations but presents challenges in nonlinear information processing. This Article demonstrates a scalable and energy-efficient nonlinear optical-computing framework that can perform machine learning tasks.

The study develops a machine learning approach for predicting bone regeneration in an additively manufactured bioceramic scaffold, which is correlated with an in vivo sheep model, exhibiting effectiveness for solving such a multiscale modeling problem.

Countries are using hospital admission policies that prioritize patients with COVID-19 during the pandemic. The authors propose an alternative open-source framework to optimally schedule hospital care for all diseases and patients that can save life years overall.

This work demonstrates that large gains still exist in accelerating and improving the coverage of reaction prediction algorithms. These advances create opportunities for computationally exploring deeper and broader reaction networks.

The authors propose a deep learning model that analyzes single-cell RNA sequencing (scRNA-seq) data by explicitly modeling gene regulatory networks (GRNs), outperforming the state-of-art methods on various tasks, including GRN inference, scRNA-seq analysis and simulation.

An evidence-based approach for dealing with insufficient, conflicting and biased materials data is proposed for recommending high-entropy alloys, showing good capabilities for extrapolating the number of components.

Deep graph neural networks can refine a predicted protein model efficiently with less computing resources. The accuracy is comparable to that of the leading physics-based methods that rely on time-consuming conformation sampling.

Near-term quantum computers hold many promises but remain limited to a moderate number of qubits. This Article presents a pathway for modeling correlated materials with a reduced number of qubits, bringing quantum computing to materials modeling.

A class of quantum neural networks is presented that outperforms comparable classical feedforward networks. They achieve a higher capacity in terms of effective dimension and at the same time train faster, suggesting a quantum advantage.

BaseQTL is a Bayesian method to map molecular QTL affecting allele-specific expression even when no genotypes are available. It is well suited to discover eQTLs hidden in a wealth of RNA-seq data to unravel molecular mechanisms underpinning disease.

A statistical modeling method is proposed to generalize right censored data to a standard regression problem, thus making it possible to apply regression learning algorithms to survival prediction problems.

Predicting binding specificity of T-cell receptors (TCRs) and putative antigens can help improve cancer immunotherapy. Lin et al. propose RACER, which efficiently makes use of supervised machine learning to learn important molecular interactions contributing to TCR–peptide binding.

A dynamic organ-resolved model is developed by integrating metabolic and regulatory processes in type 1 diabetes, providing a depiction of network dynamics, regulation and response to perturbations in relation to variability in insulin response.

A manifold-preserving feature selection method was developed for single-cell data analysis, which selects non-redundant features to help detect rare cell populations, design follow-up studies and create targeted panels.

This work proposes a probabilistic graphical model as a formal mathematical foundation for digital twins, and demonstrates how this model supports principled data assimilation, optimal control and end-to-end uncertainty quantification.

haploSep is a computationally efficient method to infer major haplotypes and their frequencies from multiple samples of allele frequency data, and to provide improved estimates of experimentally obtained allele frequencies.

The CARseq method allows users to assess cell type-specific differential expression using RNA-seq data from bulk tissue samples, which opens up several opportunities for re-analyzing existing RNA-seq data and designing new studies.

Physics-aware deep generative models are used to design material microstructures exhibiting tailored properties. Multi-fidelity data are used to create inexpensive yet accurate machine learning surrogates for evaluating the physics-based constraints within such design frameworks.