Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A framework for variation discovery and genotyping using next-generation DNA sequencing data
20117.3k citationsMark A. DePristo, Eric Banks et al.Nature Geneticsprofile →
From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline
20134.3k citationsGeraldine Van Der Auwera, Mauricio O. Carneiro et al.Current Protocols in Bioinformaticsprofile →
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
20181.0k citationsRyan Poplin, Avinash V. Varadarajan et al.Nature Biomedical Engineeringprofile →
A universal SNP and small-indel variant caller using deep neural networks
2018757 citationsRyan Poplin, Pi-Chuan Chang et al.Nature Biotechnologyprofile →
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
2018394 citationsEric Christiansen, Samuel Yang et al.Cellprofile →
Identifying viruses from metagenomic data using deep learning
2020361 citationsJie Ren, Kai Song et al.Quantitative Biologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Ryan Poplin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ryan Poplin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Poplin more than expected).
This network shows the impact of papers produced by Ryan Poplin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ryan Poplin. The network helps show where Ryan Poplin may publish in the future.
Co-authorship network of co-authors of Ryan Poplin
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Poplin.
A scholar is included among the top collaborators of Ryan Poplin based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ryan Poplin. Ryan Poplin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ren, Jie, Kai Song, Chao Deng, et al.. (2020). Identifying viruses from metagenomic data using deep learning. Quantitative Biology. 8(1). 64–77.361 indexed citations breakdown →
Christiansen, Eric, Samuel Yang, D. Michael Ando, et al.. (2018). In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell. 173(3). 792–803.e19.394 indexed citations breakdown →
Poplin, Ryan, Pi-Chuan Chang, David H. Alexander, et al.. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology. 36(10). 983–987.757 indexed citations breakdown →
8.
Poplin, Ryan, Avinash V. Varadarajan, Katy Blumer, et al.. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2(3). 158–164.1029 indexed citations breakdown →
Auwera, Geraldine Van Der, Mauricio O. Carneiro, Christopher Hartl, et al.. (2013). From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. Current Protocols in Bioinformatics. 43(1). 11.10.1–11.10.33.4252 indexed citations breakdown →
12.
DePristo, Mark A., Eric Banks, Ryan Poplin, et al.. (2011). A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics. 43(5). 491–498.7268 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.