Poplin, Ryan

Analysis of protein-coding genetic variation in 60,706 humans

Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 9,587

From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline

Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy‐Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al. (2013) · Current Protocols in Bioinformatics

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 6,687

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 1,865

A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 1,840

Patterns and rates of exonic de novo mutations in autism spectrum disorders

Neale, B. M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K. E., Sabo, A., Lin, C.-F., Stevens, C., Wang, L.-S., Makarov, V., et al. (2012) · Nature

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 1,734

The genetic architecture of type 2 diabetes

Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,051

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Christiansen, E. M., Yang, S. J., Ando, D. M., Javaherian, A., Skibinski, G., Lipnick, S., Mount, E., O’Neil, A., Shah, K., Lee, A. K., et al. (2018) · Cell

Cell Image Analysis TechniquesAdvanced Fluorescence Microscopy Techniques

Cited by 635

Effect of predicted protein-truncating genetic variants on the human transcriptome

Rivas, M. A., Pirinen, M., Conrad, D. F., Lek, M., Tsang, E. K., Karczewski, K. J., Maller, J. B., Kukurba, K. R., DeLuca, D. S., Fromer, M., et al. (2015) · Science

Genomics and Rare DiseasesRNA Research and Splicing

Cited by 313

Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls

Liu, L., Sabo, A., Neale, B. M., Nagaswamy, U., Stevens, C., Lim, E., Bodea, C. A., Muzny, D., Reid, J. G., Banks, E., et al. (2013) · PLoS Genetics

Autism Spectrum Disorder ResearchGenomics and Rare Diseases

Cited by 135

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69