Li MM, Huang Y, Sumathipala M, et al. Contextualizing protein representations using deep learning on protein networks and single-cell data. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.07.18.549602
Publications
Zhang A, Jin L, Yao S, et al. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and sequencing. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.03.16.532873
Kentistou KA, Kaisinger LR, Stankovic S, et al. Understanding the genetic complexity of puberty timing across the allele frequency spectrum. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.06.14.23291322
Yuan K, Longchamps RJ, Pardiñas AF, et al. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.01.07.23284293
Lee J, Gilliland T, Koyama S, et al. Integrative metabolomics differentiate coronary artery disease, peripheral artery disease, and venous thromboembolism risks. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.06.21.23291103
Holman CD, Sakers AP, Calhoun RP, et al. Aging impairs cold-induced beige adipogenesis and adipocyte metabolic reprogramming. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.03.20.533514
Mitchell W, Goeminne LJE, Tyshkovskiy A, et al. Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.06.30.546730
Koenig Z, Yohannes MT, Nkambule LL, et al. A harmonized public resource of deeply sequenced diverse human genomes. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.01.23.525248
Barton AR, Santander CG, Skoglund P, Moltke I, Reich D, Mathieson I. Insufficient evidence for natural selection associated with the Black Death. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.03.14.532615
Shaban M, Bai Y, Qiu H, et al. MAPS: Pathologist-level cell type annotation from tissue images through machine learning. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.06.25.546474