Shada Abuhattum

1.3k total citations
22 papers, 754 citations indexed

About

Shada Abuhattum is a scholar working on Cell Biology, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Shada Abuhattum has authored 22 papers receiving a total of 754 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cell Biology, 7 papers in Biomedical Engineering and 6 papers in Molecular Biology. Recurrent topics in Shada Abuhattum's work include Cellular Mechanics and Interactions (14 papers), Microfluidic and Bio-sensing Technologies (4 papers) and Force Microscopy Techniques and Applications (3 papers). Shada Abuhattum is often cited by papers focused on Cellular Mechanics and Interactions (14 papers), Microfluidic and Bio-sensing Technologies (4 papers) and Force Microscopy Techniques and Applications (3 papers). Shada Abuhattum collaborates with scholars based in Germany, United Kingdom and Switzerland. Shada Abuhattum's co-authors include Jochen Guck, Paul Müller, Anna Taubenberger, Stephanie Möllmert, Raimund Schlüßler, Gheorghe Cojoc, Kyoohyun Kim, Maik Herbig, Angela Jacobi and Salvatore Girardo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Development and Nature Methods.

In The Last Decade

Shada Abuhattum

20 papers receiving 747 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shada Abuhattum Germany 15 360 324 160 134 130 22 754
Kareem Elsayad Austria 13 172 0.5× 174 0.5× 160 1.0× 175 1.3× 87 0.7× 35 601
Raimund Schlüßler Germany 13 246 0.7× 214 0.7× 119 0.7× 85 0.6× 93 0.7× 26 570
Huw Colin‐York United Kingdom 20 257 0.7× 493 1.5× 233 1.5× 263 2.0× 233 1.8× 31 1.1k
Kévin Alessandri France 13 715 2.0× 284 0.9× 258 1.6× 190 1.4× 110 0.8× 16 1.0k
Martin Kräter Germany 17 436 1.2× 339 1.0× 103 0.6× 222 1.7× 76 0.6× 37 1.0k
Chau‐Hwang Lee Taiwan 18 620 1.7× 200 0.6× 138 0.9× 268 2.0× 115 0.9× 58 1.0k
Marta Urbanska Germany 9 293 0.8× 248 0.8× 85 0.5× 130 1.0× 64 0.5× 14 540
Devrim Pesen‐Okvur Türkiye 12 307 0.9× 281 0.9× 34 0.2× 147 1.1× 198 1.5× 29 752
William A. Marganski United States 11 430 1.2× 750 2.3× 89 0.6× 329 2.5× 110 0.8× 15 1.1k

Countries citing papers authored by Shada Abuhattum

Since Specialization
Citations

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

Fields of papers citing papers by Shada Abuhattum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shada Abuhattum

This figure shows the co-authorship network connecting the top 25 collaborators of Shada Abuhattum. A scholar is included among the top collaborators of Shada Abuhattum 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 Shada Abuhattum. Shada Abuhattum 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.
Müller, Paul, et al.. (2025). Small U‐Net for Fast and Reliable Segmentation in Imaging Flow Cytometry. Cytometry Part A. 107(7). 450–463.
2.
Beck, Timon, Shada Abuhattum, Kyoohyun Kim, et al.. (2024). Estimation of the mass density of biological matter from refractive index measurements. SHILAP Revista de lepidopterología. 4(2). 100156–100156. 5 indexed citations
3.
Urbanska, Marta, Yan Ge, Maria Winzi, et al.. (2023). De novo identification of universal cell mechanics gene signatures. eLife. 12. 1 indexed citations
4.
Schlüßler, Raimund, Kyoohyun Kim, Anna Taubenberger, et al.. (2022). Correlative all-optical quantification of mass density and mechanics of subcellular compartments with fluorescence specificity. eLife. 11. 57 indexed citations
5.
Trushko, Anastasiya, et al.. (2022). Pressure and curvature control of the cell cycle in epithelia growing under spherical confinement. Cell Reports. 40(8). 111227–111227. 15 indexed citations
6.
Abuhattum, Shada, et al.. (2022). Unbiased retrieval of frequency-dependent mechanical properties from noisy time-dependent signals. SHILAP Revista de lepidopterología. 2(3). 100054–100054.
7.
Abuhattum, Shada, Dominic Mokbel, Paul Müller, et al.. (2022). An explicit model to extract viscoelastic properties of cells from AFM force-indentation curves. iScience. 25(4). 104016–104016. 25 indexed citations
8.
Abuhattum, Shada, Petra Kotzbeck, Raimund Schlüßler, et al.. (2022). Adipose cells and tissues soften with lipid accumulation while in diabetes adipose tissue stiffens. Scientific Reports. 12(1). 10325–10325. 32 indexed citations
9.
Taubenberger, Anna, Shada Abuhattum, Christine Schweitzer, et al.. (2021). Compliant Substrates Enhance Macrophage Cytokine Release and NLRP3 Inflammasome Formation During Their Pro-Inflammatory Response. Frontiers in Cell and Developmental Biology. 9. 639815–639815. 36 indexed citations
10.
Kräter, Martin, Shada Abuhattum, Despina Soteriou, et al.. (2021). AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. Advanced Science. 8(11). e2003743–e2003743. 40 indexed citations
11.
Trushko, Anastasiya, Carlès Blanch-Mercader, Shada Abuhattum, et al.. (2020). Buckling of an Epithelium Growing under Spherical Confinement. Developmental Cell. 54(5). 655–668.e6. 68 indexed citations
12.
Urbanska, Marta, Maik Herbig, Martin Kräter, et al.. (2020). Intelligent image-based deformation-assisted cell sorting with molecular specificity. Nature Methods. 17(6). 595–599. 115 indexed citations
13.
Herbig, Maik, Ahmad Nawaz, Marta Urbanska, et al.. (2020). Image-based cell sorting using artificial intelligence. 4 indexed citations
14.
Möllmert, Stephanie, Thomas Höche, Anna Taubenberger, et al.. (2019). Zebrafish Spinal Cord Repair Is Accompanied by Transient Tissue Stiffening. Biophysical Journal. 118(2). 448–463. 31 indexed citations
15.
Müller, Paul, Shada Abuhattum, Stephanie Möllmert, et al.. (2019). nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data. BMC Bioinformatics. 20(1). 465–465. 38 indexed citations
16.
Schlüßler, Raimund, Stephanie Möllmert, Shada Abuhattum, et al.. (2018). Mechanical Mapping of Spinal Cord Growth and Repair in Living Zebrafish Larvae by Brillouin Imaging. Biophysical Journal. 115(5). 911–923. 120 indexed citations
17.
Urbanska, Marta, Maria Winzi, Katrin Neumann, et al.. (2018). Single-Cell Mechanical Phenotype is an Intrinsic Marker of Reprogramming and Differentiation along the Neural Lineage. Biophysical Journal. 114(3). 516a–517a. 1 indexed citations
18.
Abuhattum, Shada, Kyoohyun Kim, Titus M. Franzmann, et al.. (2018). Intracellular Mass Density Increase Is Accompanying but Not Sufficient for Stiffening and Growth Arrest of Yeast Cells. Frontiers in Physics. 6. 19 indexed citations
19.
Girardo, Salvatore, Nicole Träber, Katrin Wagner, et al.. (2018). Standardized microgel beads as elastic cell mechanical probes. Journal of Materials Chemistry B. 6(39). 6245–6261. 83 indexed citations
20.
Urbanska, Marta, Maria Winzi, Katrin Neumann, et al.. (2017). Single-cell mechanical phenotype is an intrinsic marker of reprogramming and differentiation along the mouse neural lineage. Development. 144(23). 4313–4321. 27 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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