Keep up with the field without drowning in PDFs. The strongest new open-access research — genomics, single-cell, proteomics, systems biology and beyond — each turned into a clear, few-minute read you can trust. Three new papers a week.
The deep learning tool DeepID uses two convolutional neural networks to integrate genomic and trait data, improving species delimitation accuracy in complex evolutionary scenarios.
A comparison of five single-cell transcriptomics methods on human placenta reveals that sample processing drastically alters detected cell-type proportions, while in situ combinatorial indexing shows a strong, unexpected bias toward nuclear rather than cytoplasmic RNA.
CAMUS is a new scalable algorithm for estimating phylogenetic networks that can analyze datasets of up to 201 species in hours, overcoming the strict computational limits of previous methods.
AI protein structure prediction models like AlphaFold2 incorrectly predict that ionizable residues can be buried in nonpolar cores for mutated sequences, a physical impossibility that can be corrected using short molecular dynamics simulations.
The foundation model yHydra jointly embeds mass spectrometry spectra and peptide sequences into a shared space, enabling open and error-tolerant searches that increase peptide identification by up to 25% compared to existing methods.
GenVarLoader is a new dataloader that creates personalized genomic sequences on-the-fly, reading variants up to 26,000 times faster and reducing storage by 2,000-fold compared to existing methods.
The authors developed a multimodal contrastive learning model for Electronic Health Records that mathematically connects the loss function to the singular value decomposition of a pointwise mutual information matrix, enabling a novel privacy-preserving algorithm.