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Emerging patterns in rodent-borne zoonotic diseases | Science

Rodents are ubiquitous and typically unwelcome dwellers in human habitats worldwide, infesting homes, farm fields, and agricultural stores and potentially shedding disease-causing microbes into the most human-occupied...

Biophysically interpretable inference of cell types from multimodal sequencing data

La Manno, G. et al. Molecular architecture of the developing mouse brain. Nature 596, 92–96 (2021).Chari, T. et al. Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across...

Leveraging gene correlations in single cell transcriptomic data | BMC Bioinformatics

The significance of gene–gene correlationsThe statistical significance of correlations is rarely discussed because, for many common kinds of data—those that are continuous and at least approximately normal...

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Search and match across spatial omics samples at single-cell resolution

Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse...

Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Construction of G2P3D APIWe integrated public databases focusing on genes, transcripts and proteins to build an API for seamless mapping of identifiers for genes,...

Cryptic population structure and insecticide resistance in Anopheles gambiae from the southern Democratic Republic of Congo

Adult mosquitoes were collected for sequencing, and larval stages collected and reared to adulthood for phenotypic assays, at the sites shown in Fig. 1.Fig. 1Map...

Protein interactions in human pathogens revealed through deep learning

Computational pipeline for proteome-wide PPI identificationTo screen through hundreds of millions of protein pairs for PPIs, we first sought to increase the computational efficiency...

Pre-training with fractional denoising to enhance molecular property prediction

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).Article  ...

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