Research articles

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  • 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.

    • Xingcheng Lin
    • Jason T. George
    • Herbert Levine
  • 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.

    • Marouen Ben Guebila
    • Ines Thiele
  • 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.

    • Robert Vaser
    • Mile Šikić
    Brief Communication
  • 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.

    • Diego A. Espinoza
    • Ryland D. Mortlock
    • Cynthia E. Dunbar
  • 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.

    • Jacob A. Blindenbach
    • Karthik A. Jagadeesh
    • David J. Wu
  • 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.

    • Marta Pelizzola
    • Merle Behr
    • Andreas Futschik
  • 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.

    • Chong Jin
    • Mengjie Chen
    • Wei Sun
  • 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.

    • Xian Yeow Lee
    • Joshua R. Waite
    • Soumik Sarkar
  • 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.

    • Carla Tameling
    • Stefan Stoldt
    • Axel Munk
  • 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.

    • Bin Li
    • Peijun Chen
    • Jun Zhang
  • 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.

    • Dennis Grishin
    • Jean Louis Raisaro
    • Jean-Pierre Hubaux
    Brief Communication
  • 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.

    • Wouter Edeling
    • Hamid Arabnejad
    • Peter V. Coveney
  • 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.

    • Alexander G. B. Grønning
    • Mhaned Oubounyt
    • Jan Baumbach
  • 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.

    • James C. Knight
    • Thomas Nowotny