John Healy

17.5k total citations · 4 hit papers
6 papers, 9.0k citations indexed

About

John Healy is a scholar working on Molecular Biology, Structural Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, John Healy has authored 6 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Molecular Biology, 1 paper in Structural Biology and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in John Healy's work include Data-Driven Disease Surveillance (1 paper), Single-cell and spatial transcriptomics (1 paper) and Surface and Thin Film Phenomena (1 paper). John Healy is often cited by papers focused on Data-Driven Disease Surveillance (1 paper), Single-cell and spatial transcriptomics (1 paper) and Surface and Thin Film Phenomena (1 paper). John Healy collaborates with scholars based in United States, Netherlands and Singapore. John Healy's co-authors include Leland McInnes, Lukas Großberger, Nathaniel Saul, Lai Guan Ng, Evan W. Newell, Étienne Becht, Immanuel Kwok, Charles‐Antoine Dutertre, Florent Ginhoux and Mark P. Oxley and has published in prestigious journals such as Nature Biotechnology, npj Computational Materials and Nature Reviews Methods Primers.

In The Last Decade

John Healy

6 papers receiving 8.8k citations

Hit Papers

align trajectories

What are hit papers?

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.

UMAP: Uniform Manifold Approximation and Projection

2018 4.6k citations Leland McInnes, John Healy et al. The Journal of Open Source Software profile →
Dimensionality reduction for visualizing single-cell data using UMAP

2018 2.9k citations Étienne Becht, Leland McInnes et al. Nature Biotechnology profile →
hdbscan: Hierarchical density based clustering

2017 1.4k citations Leland McInnes, John Healy et al. The Journal of Open Source Software profile →
Uniform manifold approximation and projection

2024 70 citations John Healy, Leland McInnes Nature Reviews Methods Primers profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
John Healy United States	5	3.8k	1.4k	1.1k	730	720	6	9.0k
Leland McInnes United States	7	3.8k 1.0×	1.4k 1.0×	1.1k 1.0×	730 1.0×	726 1.0×	9	9.0k
Pierre Geurts Belgium	34	4.6k 1.2×	2.5k 1.8×	1.3k 1.1×	879 1.2×	1.0k 1.4×	104	13.7k
James Zou United States	54	3.2k 0.8×	3.4k 2.5×	411 0.4×	544 0.7×	633 0.9×	240	12.1k
Píetro Lió United Kingdom	53	5.8k 1.5×	1.5k 1.1×	865 0.8×	347 0.5×	506 0.7×	444	12.8k
Frederick Klauschen Germany	46	2.8k 0.7×	2.1k 1.5×	2.5k 2.2×	1.5k 2.0×	656 0.9×	201	10.5k
Marcel Reinders Netherlands	54	5.9k 1.5×	1.2k 0.9×	633 0.6×	1.0k 1.4×	1.1k 1.6×	298	11.1k
Hiroaki Kitano Japan	52	8.7k 2.3×	1.6k 1.2×	787 0.7×	531 0.7×	955 1.3×	285	15.6k
Nathaniel Saul United States	5	1.8k 0.5×	837 0.6×	379 0.3×	328 0.4×	517 0.7×	6	4.7k
Lukas Großberger Germany	3	1.8k 0.5×	820 0.6×	379 0.3×	327 0.4×	486 0.7×	4	4.6k
Yvan Saeys Belgium	49	8.5k 2.2×	2.1k 1.5×	4.2k 3.7×	1.1k 1.6×	1.0k 1.4×	196	16.4k

Countries citing papers authored by John Healy

Since Specialization

Citations

This map shows the geographic impact of John Healy'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 John Healy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Healy more than expected).

Fields of papers citing papers by John Healy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Healy. 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 John Healy. The network helps show where John Healy may publish in the future.

Co-authorship network of co-authors of John Healy

This figure shows the co-authorship network connecting the top 25 collaborators of John Healy. A scholar is included among the top collaborators of John Healy 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 John Healy. John Healy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Healy, John & Leland McInnes. (2024). Uniform manifold approximation and projection. Nature Reviews Methods Primers. 4(1). 70 indexed citations breakdown →

2.

Dyck, Ondrej, Mark P. Oxley, Andrew R. Lupini, et al.. (2020). Author Correction: Manifold learning of four-dimensional scanning transmission electron microscopy. npj Computational Materials. 6(1). 1 indexed citations

3.

Dyck, Ondrej, Mark P. Oxley, Andrew R. Lupini, et al.. (2018). Manifold learning of four-dimensional scanning transmission electron microscopy. npj Computational Materials. 5(1). 45 indexed citations

4.

Becht, Étienne, Leland McInnes, John Healy, et al.. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology. 37(1). 38–44. 2943 indexed citations breakdown →

5.

McInnes, Leland, John Healy, Nathaniel Saul, & Lukas Großberger. (2018). UMAP: Uniform Manifold Approximation and Projection. The Journal of Open Source Software. 3(29). 861–861. 4554 indexed citations breakdown →

6.

McInnes, Leland, et al.. (2017). hdbscan: Hierarchical density based clustering. The Journal of Open Source Software. 2(11). 205–205. 1359 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.

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