Zhenqin Wu

4.3k total citations · 1 hit paper
16 papers, 2.4k citations indexed

About

Zhenqin Wu is a scholar working on Molecular Biology, Biophysics and Artificial Intelligence. According to data from OpenAlex, Zhenqin Wu has authored 16 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 5 papers in Biophysics and 3 papers in Artificial Intelligence. Recurrent topics in Zhenqin Wu's work include Single-cell and spatial transcriptomics (5 papers), Cell Image Analysis Techniques (4 papers) and Machine Learning in Materials Science (3 papers). Zhenqin Wu is often cited by papers focused on Single-cell and spatial transcriptomics (5 papers), Cell Image Analysis Techniques (4 papers) and Machine Learning in Materials Science (3 papers). Zhenqin Wu collaborates with scholars based in United States, China and Germany. Zhenqin Wu's co-authors include Bharath Ramsundar, Vijay S. Pande, Evan N. Feinberg, Joseph Gomes, Karl Leswing, Caleb Geniesse, Jianyi Yang, Saisai Sun, Brooke E. Husic and Yang Li and has published in prestigious journals such as Nature Communications, Physiological Reviews and Nature reviews. Cancer.

In The Last Decade

Zhenqin Wu

15 papers receiving 2.4k 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.

MoleculeNet: a benchmark for molecular machine learning

2017 1.7k citations Zhenqin Wu, Bharath Ramsundar et al. Chemical Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Zhenqin Wu United States	9	1.6k	1.3k	1.3k	339	121	16	2.4k
Karl Leswing United States	11	1.5k 0.9×	1.3k 1.0×	1.1k 0.9×	304 0.9×	115 1.0×	14	2.3k
Evan N. Feinberg United States	9	1.7k 1.0×	1.3k 1.0×	2.0k 1.5×	306 0.9×	161 1.3×	17	3.2k
Thomas Blaschke Germany	11	1.9k 1.2×	1.3k 1.0×	1.4k 1.1×	227 0.7×	99 0.8×	13	2.6k
Caleb Geniesse United States	6	1.2k 0.8×	1.1k 0.8×	864 0.7×	289 0.9×	94 0.8×	9	1.8k
Joseph Gomes United States	10	1.5k 0.9×	1.6k 1.2×	1.2k 0.9×	376 1.1×	144 1.2×	26	2.6k
Djork-Arné Clevert Germany	18	1.1k 0.7×	765 0.6×	1.5k 1.1×	197 0.6×	92 0.8×	38	2.4k
Marcus Olivecrona Sweden	3	1.5k 0.9×	980 0.7×	1.1k 0.9×	203 0.6×	77 0.6×	3	2.1k
Jörg K. Wegner Belgium	26	1.5k 0.9×	668 0.5×	1.4k 1.1×	197 0.6×	230 1.9×	49	2.3k
Marwin Segler United Kingdom	14	2.0k 1.2×	1.9k 1.4×	1.3k 1.0×	295 0.9×	106 0.9×	21	3.2k
Esben Jannik Bjerrum Sweden	21	1.6k 1.0×	1.3k 1.0×	1.2k 0.9×	137 0.4×	122 1.0×	39	2.2k

Countries citing papers authored by Zhenqin Wu

Since Specialization

Citations

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

Fields of papers citing papers by Zhenqin Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenqin Wu

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

All Works

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16 of 16 papers shown

Wu, Eric Q., Zhenqin Wu, Gregory W. Charville, et al.. (2025). ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images. Nature Communications. 16(1). 7633–7633. 2 indexed citations

Kondo, Ayano, Alexandro E. Trevino, Zhenqin Wu, et al.. (2024). Spatial proteomics of human diabetic kidney disease, from health to class III. Diabetologia. 67(9). 1962–1979. 5 indexed citations

Wu, Zhenqin, Ayano Kondo, Benjamin Chidester, et al.. (2024). Discovery and generalization of tissue structures from spatial omics data. Cell Reports Methods. 4(8). 100838–100838. 4 indexed citations

Wu, Zhenqin. (2023). Identifying spatial cellular structures with SPACE-GM. Nature reviews. Cancer. 23(8). 508–508. 1 indexed citations

Wu, Eric Q., Alexandro E. Trevino, Zhenqin Wu, et al.. (2023). 7-UP: Generating in silico CODEX from a small set of immunofluorescence markers. PNAS Nexus. 2(6). pgad171–pgad171. 12 indexed citations

Wu, Eric Q., Zhenqin Wu, Aaron T. Mayer, Alexandro E. Trevino, & James Zou. (2023). PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement. The HKU Scholars Hub (University of Hong Kong). 492–505.

Zhang, Angela, Zhenqin Wu, Eric Q. Wu, et al.. (2023). Leveraging physiology and artificial intelligence to deliver advancements in health care. Physiological Reviews. 103(4). 2423–2450. 15 indexed citations

Wu, Zhenqin, Bryant B. Chhun, Galina Popova, et al.. (2022). DynaMorph: self-supervised learning of morphodynamic states of live cells. Molecular Biology of the Cell. 33(6). ar59–ar59. 24 indexed citations

Wu, Zhenqin, Alexandro E. Trevino, Eric Q. Wu, et al.. (2022). Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nature Biomedical Engineering. 6(12). 1435–1448. 67 indexed citations

10.

Wu, Zhenqin, Daniel Serie, Gege Xu, & James Zou. (2020). PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring. Journal of Proteomics. 223. 103820–103820. 19 indexed citations

11.

Wu, Zhenqin, Nilah M. Ioannidis, & James Zou. (2020). Predicting target genes of non-coding regulatory variants with IRT. Bioinformatics. 36(16). 4440–4448. 5 indexed citations

12.

Leenay, Ryan T., Amirali Aghazadeh, Joseph Hiatt, et al.. (2019). Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells. Nature Biotechnology. 37(9). 1034–1037. 82 indexed citations

13.

Feinberg, Evan N., Zhenqin Wu, Brooke E. Husic, et al.. (2018). PotentialNet for Molecular Property Prediction. ACS Central Science. 4(11). 1520–1530. 298 indexed citations

14.

Wu, Zhenqin, Bharath Ramsundar, Evan N. Feinberg, et al.. (2017). MoleculeNet: a benchmark for molecular machine learning. Chemical Science. 9(2). 513–530. 1696 indexed citations breakdown →

15.

Ramsundar, Bharath, Bowen Liu, Zhenqin Wu, et al.. (2017). Is Multitask Deep Learning Practical for Pharma?. Journal of Chemical Information and Modeling. 57(8). 2068–2076. 183 indexed citations

16.

Wu, Zhenqin, et al.. (2016). Determination of Equilibrium Constant and Relative Brightness in Fluorescence Correlation Spectroscopy by Considering Third-Order Correlations. The Journal of Physical Chemistry B. 120(45). 11674–11682. 5 indexed citations

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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