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Detection of molecular quantitative trait loci (QTL) facilitates mechanistic insights into disease-associated genetic variants. A new study describes BaseQTL, which exploits allele-specific expression to map molecular QTL from sequencing reads even without paired genotype data.
A study based on effective dimension shows that a quantum neural network can have increased capability and trainability as compared to its classical counterpart.
Multi-omics studies have been increasingly used to better understand biological samples and infer molecular interactions. Nevertheless, a number of challenges must still be addressed to take full advantage of multi-omics data and to avoid reaching potentially incorrect conclusions.
In this issue, a large and multiscale whole-body model of organ-specific regulation and metabolism for type 1 diabetes is developed, providing important details on glucose and insulin dynamics.
Development in digital-twin technology has been rapidly growing across a range of industries and disciplines. However, to ensure a wider and more robust adoption of such technology, various challenges must be addressed by the computational science community.
A uniform mathematical framework based on probabilistic graphical models drives the digital twin technologies towards dynamical control with real-time data.
The field of biomolecular modeling has thrived by exploiting state-of-the-art technological advances. In this Perspective, the role of software and hardware advances, and the disparity and synergy between knowledge-based and physics-based methods are discussed and explored.
Mapping X-ray diffraction patterns to crystal structures is a comprehensive and time-consuming task for chemists and materials scientists. In a recent work, researchers developed a machine-learning tool to make this job more ‘self-driving’.
In this issue, a protocol for querying criminal DNA databases is developed to prevent profile discovery, therefore reducing the potential for racial bias.
Analyzing cellular barcoding data is challenging due to the absence of a flexible, complete and easy-to-use set of tools that can help scientists derive biological meaning from these datasets. barcodetrackR is a promising package for filling this gap in the field.
A limitation of data obtained from RNA-seq experiments is the presence of different types of cell expression, making it difficult to identify the contribution of cell-type composition or cell-type-specific expression. A new study addresses this problem by proposing a method for cell-type-aware analysis of RNA-seq data.
The computational complexity of deep neural networks is a major obstacle of many application scenarios driven by low-power devices, including federated learning. A recent finding shows that random sketches can substantially reduce the model complexity without affecting prediction accuracy.
Sharing of genomic data poses a problem due to privacy concerns and lack of individual’s choice in the matter. In this issue, researchers propose a framework for sharing data where the control lies with the individual.
A model for the electrical double layer at solid-state electrochemical interfaces is reported, shedding some light on the design and optimization of future all-solid-state Li-ion batteries.
The advent of STED microscopy, which allows observation at a sub-diffraction resolution, raises a challenge in studying spatial proximities of biomolecules’ distributions. In this issue, researchers have attempted to study colocalization of molecules by employing optimal transport.
Massive datasets have been made available to enable systematic studies of gene regulation and its control via epigenetic mechanisms. In this Review, state-of-the-art computational methods used to effectively extract knowledge from these datasets are presented and discussed.
The identification of genes that are associated with developmental trajectories of cells is an important focus in single-cell transcriptomics. This issue presents Scellnetor, a resource that facilitates this task.
Understanding and quantifying the uncertainty of predictions from COVID-19 pandemic models is essential to inform public health decision making. This issue presents one such examination using the influential CovidSim model.
Obtaining a consistent taxonomy of neuron types is challenging mainly because of the high dimensionality of the datasets. Coupled autoencoders are a step forward in achieving this goal.
