Marcelo Cicconet

2.7k total citations · 1 hit paper
29 papers, 1.3k citations indexed

About

Marcelo Cicconet is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Biophysics. According to data from OpenAlex, Marcelo Cicconet has authored 29 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 10 papers in Molecular Biology and 6 papers in Biophysics. Recurrent topics in Marcelo Cicconet's work include Cell Image Analysis Techniques (6 papers), Single-cell and spatial transcriptomics (5 papers) and Music Technology and Sound Studies (4 papers). Marcelo Cicconet is often cited by papers focused on Cell Image Analysis Techniques (6 papers), Single-cell and spatial transcriptomics (5 papers) and Music Technology and Sound Studies (4 papers). Marcelo Cicconet collaborates with scholars based in United States, Brazil and United Kingdom. Marcelo Cicconet's co-authors include Bernardo L. Sabatini, Siniša Hrvatin, Michael E. Greenberg, Daniel R. Hochbaum, M. Aurel Nagy, Keiramarie Robertson, Lucas Cheadle, Rebeca Borges-Monroy, Rapolas Žilionis and Allon M. Klein and has published in prestigious journals such as Nature, Nature Communications and The Journal of Cell Biology.

In The Last Decade

Marcelo Cicconet

27 papers receiving 1.3k citations

Hit Papers

Immuno-SABER enables highly multiplexed and amplified pro... 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcelo Cicconet United States 13 759 244 171 130 119 29 1.3k
Talley J. Lambert United States 18 733 1.0× 299 1.2× 167 1.0× 117 0.9× 102 0.9× 23 1.5k
Sam Vesuna United States 10 959 1.3× 332 1.4× 269 1.6× 257 2.0× 87 0.7× 14 1.5k
Ye Li China 24 768 1.0× 354 1.5× 366 2.1× 200 1.5× 152 1.3× 72 2.1k
Megumi Eguchi Canada 13 554 0.7× 323 1.3× 476 2.8× 210 1.6× 99 0.8× 18 1.6k
Christoph Kirst United States 14 589 0.8× 315 1.3× 254 1.5× 330 2.5× 118 1.0× 23 1.5k
Uri Manor United States 22 1.1k 1.5× 133 0.5× 123 0.7× 67 0.5× 151 1.3× 54 2.0k
Tao Jiang China 23 551 0.7× 226 0.9× 320 1.9× 208 1.6× 144 1.2× 92 1.6k
Kana Namiki Japan 12 630 0.8× 197 0.8× 251 1.5× 122 0.9× 65 0.5× 20 1.2k
César S. Mendes Portugal 11 442 0.6× 533 2.2× 282 1.6× 83 0.6× 94 0.8× 21 1.4k
Stephen W. Eichhorn United States 13 2.2k 2.9× 128 0.5× 192 1.1× 138 1.1× 177 1.5× 16 2.8k

Countries citing papers authored by Marcelo Cicconet

Since Specialization
Citations

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

Fields of papers citing papers by Marcelo Cicconet

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcelo Cicconet

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

All Works

20 of 20 papers shown
1.
Mizrak, Arda, Chia‐Wei Lee, Marcelo Cicconet, et al.. (2022). Identification of two pathways mediating protein targeting from ER to lipid droplets. Nature Cell Biology. 24(9). 1364–1377. 55 indexed citations
2.
Yapp, Clarence, Edward Novikov, Won-Dong Jang, et al.. (2022). UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. Communications Biology. 5(1). 1263–1263. 20 indexed citations
3.
Wu, David M., Maryna V. Ivanchenko, Michelle Chung, et al.. (2021). Nrf2 overexpression rescues the RPE in mouse models of retinitis pigmentosa. JCI Insight. 6(2). 43 indexed citations
4.
Becuwe, Michel, Laura M. Bond, Antônio F. M. Pinto, et al.. (2020). FIT2 is an acyl–coenzyme A diphosphatase crucial for endoplasmic reticulum homeostasis. The Journal of Cell Biology. 219(10). 39 indexed citations
5.
Hrvatin, Siniša, Marcelo Cicconet, Elena G. Assad, et al.. (2020). Neurons that regulate mouse torpor. Nature. 583(7814). 115–121. 142 indexed citations
6.
Ivanchenko, Maryna V., et al.. (2020). Serial scanning electron microscopy of anti-PKHD1L1 immuno-gold labeled mouse hair cell stereocilia bundles. Scientific Data. 7(1). 182–182. 6 indexed citations
7.
Toepfer, Christopher N., Arun Sharma, Marcelo Cicconet, et al.. (2019). SarcTrack: an adaptable software tool for efficient large-scale analysis of sarcomere function in hiPSC-cardiomyocytes. MDC Repository (Max-Delbrueck-Center for Molecular Medicine). 69 indexed citations
8.
Saka, Sinem K., Yu Wang, Jocelyn Y. Kishi, et al.. (2019). Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Nature Biotechnology. 37(9). 1080–1090. 316 indexed citations breakdown →
9.
Wu, Xudong, et al.. (2019). PKHD1L1 is a coat protein of hair-cell stereocilia and is required for normal hearing. Nature Communications. 10(1). 3801–3801. 30 indexed citations
10.
Hrvatin, Siniša, Daniel R. Hochbaum, M. Aurel Nagy, et al.. (2017). Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nature Neuroscience. 21(1). 120–129. 322 indexed citations
11.
Cicconet, Marcelo, et al.. (2017). Bots for Software-Assisted Analysis of Image-Based Transcriptomics. 134–142. 6 indexed citations
12.
Cicconet, Marcelo, Michelle Gutwein, Kristin C. Gunsalus, & Davi Geiger. (2014). Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development. Computers in Biology and Medicine. 51. 24–34. 13 indexed citations
13.
Cicconet, Marcelo, Davi Geiger, Kristin C. Gunsalus, & Michael Werman. (2014). Mirror Symmetry Histograms for Capturing Geometric Properties in Images. 2981–2986. 11 indexed citations
14.
Cicconet, Marcelo, et al.. (2013). Wavelet-based Circular Hough Transform and Its Application in Embryo Development Analysis. 669–674. 8 indexed citations
15.
Cicconet, Marcelo, et al.. (2013). Human-Robot Percussion Ensemble: Anticipation on the Basis of Visual Cues. IEEE Robotics & Automation Magazine. 20(4). 105–110. 7 indexed citations
16.
Cicconet, Marcelo, et al.. (2012). DEVELOPING AND COMPOSING FOR A ROBOTIC MUSICIAN USING DIFFERENT MODES OF INTERACTION. The Journal of the Abraham Lincoln Association. 5 indexed citations
17.
Velho, Luiz, et al.. (2011). Scalable motion-aware panoramic videos. 1–2. 4 indexed citations
18.
Cicconet, Marcelo, et al.. (2011). Filter based deghosting for exposure fusion video. 1–1. 2 indexed citations
19.
Cicconet, Marcelo, et al.. (2011). Towards Mobile HDR Video. Eurographics. 75–76. 4 indexed citations
20.
Cicconet, Marcelo, et al.. (2010). Plane Tessellation With Musical Scale Tiles And Bidimensional Automatic Composition. The Journal of the Abraham Lincoln Association. 2010.

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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