Research articles

Raven is designed to democratize genome assembly, being a simple and efficient tool while keeping high accuracy. Using a method for detection of false overlaps based on graph drawing, it can be employed for various genome sizes.

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.

The R package barcodetrackR facilitates the analysis and visualization of clonal tracking data, and it includes a graphical user interface to allow researchers without programming experience to utilize various quantitative tools.

DNA profiles of suspects that are searched in criminal databases, such as CODIS, are often retained and can result in racial profiling. This work builds a privacy-preserving CODIS query system to support DNA profile searches while preserving individual privacy.

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.

Crystallography companion agent enables autonomous characterization of powder X-ray diffraction data for organic and inorganic materials.

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.

Stimulated emission depletion (STED) microscopy allows images to be captured at a subdiffraction resolution. Here optimal transport colocalization is proposed for analyzing macromolecule distributions in high-resolution STED images.

Developing lightweight deep neural networks, while essential for edge computing, still remains a challenge. Random sketch learning is a method that creates computationally efficient and compact networks, thus paving the way for deploying tiny machine learning (TinyML) in resource-constrained devices.

A self-consistent model that bridges electrochemistry and solid-state physics is developed to fully describe the ion and electron distribution at solid electrode/electrolyte interfaces and applied to lower interfacial resistance in batteries.

Sharing of personal genomics data raises privacy concerns due to the sensitive nature of the data. A citizen-centric approach involving cryptographic privacy-preserving technologies and blockchains is proposed to address this problem.

Through parametric sensitivity analysis and uncertainty quantification of the CovidSim model, a subset of this model’s parameters is identified to which the code output is most sensitive. Using these allows better and more informed decisions about proposed policies.

Analysis and interpretation of relationships across different modalities of biomedical datasets is a critical but challenging task. SiMLR is a joint dimensionality reduction algorithm that is designed to perform this task.

Scellnetor is a single-cell network enrichment method that can unravel connected molecular interaction networks to explain the progression and differentiation of developmental trajectories on a systems biology level.

Aligning multimodal data for cell type research is a challenging problem in neuroscience. Coupled autoencoders, a deep neural network-based methodology, can be used to effectively address this task.

Spiking neural network simulations are very memory-intensive, limiting large-scale brain simulations to high-performance computer systems. Knight and Nowotny propose using procedural connectivity to substantially reduce the memory footprint of these models, such that they can run on standard GPUs.

Using voice-based technologies, ChemVox is able to answer quantum chemistry questions in seconds, thus making such complex questions more accessible to the community.

The FDRnet method addresses several issues in cancer pathway analysis, including those related to detecting functionally homogeneous subnetworks, controlling the false discovery rate of the genes and handling the computational complexity.