Daniel C. Castro

438 total citations
11 papers, 305 citations indexed

About

Daniel C. Castro is a scholar working on Spectroscopy, Molecular Biology and Biophysics. According to data from OpenAlex, Daniel C. Castro has authored 11 papers receiving a total of 305 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Spectroscopy, 5 papers in Molecular Biology and 4 papers in Biophysics. Recurrent topics in Daniel C. Castro's work include Mass Spectrometry Techniques and Applications (6 papers), Cell Image Analysis Techniques (3 papers) and Ion-surface interactions and analysis (2 papers). Daniel C. Castro is often cited by papers focused on Mass Spectrometry Techniques and Applications (6 papers), Cell Image Analysis Techniques (3 papers) and Ion-surface interactions and analysis (2 papers). Daniel C. Castro collaborates with scholars based in United States and Spain. Daniel C. Castro's co-authors include Jonathan V. Sweedler, Stanislav S. Rubakhin, Constantino Antonio García Martínez, Tomás Teijeiro, Elena V. Romanova, Fan Lam, Joshua New, Christopher L. Hendrickson, Chad R. Weisbrod and Dong Jia and has published in prestigious journals such as The Plant Cell, Analytical Chemistry and Nature Methods.

In The Last Decade

Daniel C. Castro

11 papers receiving 299 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel C. Castro United States	9	146	139	81	53	32	11	305
Emma Bluemke United Kingdom	10	109 0.7×	131 0.9×	2 0.0×	52 1.0×	61 1.9×	18	371
Jaeah Kim United States	10	206 1.4×	36 0.3×	6 0.1×	15 0.3×	32 1.0×	16	304
J.-M. Vesin Switzerland	12	84 0.6×	8 0.1×	222 2.7×	40 0.8×	45 1.4×	35	363
Joseph M. Starobin United States	10	93 0.6×	4 0.0×	211 2.6×	38 0.7×	15 0.5×	31	354
Anu Vaikkinen Finland	12	141 1.0×	241 1.7×	23 0.3×	2 0.0×	67 2.1×	19	363
Michael Clerx United Kingdom	13	315 2.2×	13 0.1×	393 4.9×	22 0.4×	25 0.8×	28	515
Ilija Uzelac United States	11	99 0.7×	3 0.0×	260 3.2×	49 0.9×	24 0.8×	42	400
Rosemary M. Onjiko United States	8	360 2.5×	363 2.6×	6 0.1×	2 0.0×	123 3.8×	10	498
Warren D. Reynolds United States	7	52 0.4×	31 0.2×	8 0.1×	8 0.2×	19 0.6×	13	113

Countries citing papers authored by Daniel C. Castro

Since Specialization

Citations

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

Fields of papers citing papers by Daniel C. Castro

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel C. Castro. 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 Daniel C. Castro. The network helps show where Daniel C. Castro may publish in the future.

Co-authorship network of co-authors of Daniel C. Castro

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

All Works

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11 of 11 papers shown

Jia, Dong, Stephan Lane, Daniel C. Castro, et al.. (2025). Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping. The Plant Cell. 37(2). 5 indexed citations

Castro, Daniel C., et al.. (2024). Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nature Methods. 21(3). 521–530. 34 indexed citations

Castro, Daniel C., et al.. (2023). Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements. Mass Spectrometry Reviews. 43(4). 888–912. 8 indexed citations

Castro, Daniel C., et al.. (2023). Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry. Journal of Proteome Research. 22(2). 491–500. 8 indexed citations

Castro, Daniel C., et al.. (2023). Single-Cell and Subcellular Analysis Using Ultrahigh Resolution 21 T MALDI FTICR Mass Spectrometry. Analytical Chemistry. 95(17). 6980–6988. 9 indexed citations

Castro, Daniel C., et al.. (2022). Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling. Analytical Chemistry. 94(13). 5335–5343. 19 indexed citations

Castro, Daniel C., et al.. (2021). Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization. Nature Methods. 18(10). 1233–1238. 69 indexed citations

Castro, Daniel C., et al.. (2020). Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning. Analytical Chemistry. 92(13). 9338–9347. 59 indexed citations

Castro, Daniel C., et al.. (2020). Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach. Journal of the American Society for Mass Spectrometry. 31(11). 2338–2347. 9 indexed citations

10.

Teijeiro, Tomás, et al.. (2017). Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records. Computing in cardiology. 82 indexed citations

11.

Castro, Daniel C. & Joshua New. (2016). The Promise of Artificial Intelligence: 70 Real-World Examples. 3 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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