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.
Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
20181.6k citationsAmit V. Khera, Mark Chaffin et al.Nature Geneticsprofile →
Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood
2019432 citationsAmit V. Khera, Mark Chaffin et al.Cellprofile →
Transfer learning enables predictions in network biology
2023383 citationsChristina V. Theodoris, Ling Xiao et al.Natureprofile →
Transcriptional and Cellular Diversity of the Human Heart
2020332 citationsNathan R. Tucker, Mark Chaffin et al.Circulationprofile →
Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender
2023218 citationsStephen J. Fleming, Mark Chaffin et al.profile →
Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy
2022180 citationsMark Chaffin, Irinna Papangeli et al.Natureprofile →
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 Mark Chaffin'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 Mark Chaffin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Chaffin more than expected).
This network shows the impact of papers produced by Mark Chaffin. 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 Mark Chaffin. The network helps show where Mark Chaffin may publish in the future.
Co-authorship network of co-authors of Mark Chaffin
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Chaffin.
A scholar is included among the top collaborators of Mark Chaffin 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 Mark Chaffin. Mark Chaffin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pirruccello, James P., Joel Rämö, Seung Hoan Choi, et al.. (2023). The Genetic Determinants of Aortic Distention. Journal of the American College of Cardiology. 81(14). 1320–1335.14 indexed citations
5.
Theodoris, Christina V., Ling Xiao, Anant Chopra, et al.. (2023). Transfer learning enables predictions in network biology. Nature. 618(7965). 616–624.383 indexed citations breakdown →
Tucker, Nathan R., Mark Chaffin, Stephen J. Fleming, et al.. (2020). Transcriptional and Cellular Diversity of the Human Heart. Circulation. 142(5). 466–482.332 indexed citations breakdown →
Hindy, George, Krishna G. Aragam, Mark Chaffin, et al.. (2019). Abstract 16565: Integration Of A Genome-wide Polygenic Score With ACC/AHA Pooled Cohorts Equation In Prediction Of Coronary Artery Disease Events In >285,000 Participants. Circulation.3 indexed citations
Khera, Amit V., Mark Chaffin, Kaitlin H. Wade, et al.. (2019). Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 177(3). 587–596.e9.432 indexed citations breakdown →
15.
Khera, Amit V., Mark Chaffin, Krishna G. Aragam, et al.. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics. 50(9). 1219–1224.1631 indexed citations breakdown →
Klarin, Derek, Scott M. Damrauer, Kelly Cho, et al.. (2018). Genetics of blood lipids among similar to 300,000 multi-ethnic participants of the Million Veteran Program. Nature Genetics. 50(11).
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.