Theodore Sakellaropoulos

4.8k total citations · 1 hit paper
30 papers, 2.5k citations indexed

About

Theodore Sakellaropoulos is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Theodore Sakellaropoulos has authored 30 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 10 papers in Computational Theory and Mathematics and 5 papers in Artificial Intelligence. Recurrent topics in Theodore Sakellaropoulos's work include Computational Drug Discovery Methods (10 papers), Bioinformatics and Genomic Networks (8 papers) and Genomics and Chromatin Dynamics (7 papers). Theodore Sakellaropoulos is often cited by papers focused on Computational Drug Discovery Methods (10 papers), Bioinformatics and Genomic Networks (8 papers) and Genomics and Chromatin Dynamics (7 papers). Theodore Sakellaropoulos collaborates with scholars based in United States, Greece and United Kingdom. Theodore Sakellaropoulos's co-authors include Aristotelis Tsirigos, David Fenyö, Matija Snuderl, Narges Razavian, Nicolas Coudray, Navneet Narula, André L. Moreira, Paolo Ocampo, Leonidas G. Alexopoulos and Iannis Aifantis and has published in prestigious journals such as Nature Medicine, Nature Communications and Nature Biotechnology.

In The Last Decade

Theodore Sakellaropoulos

30 papers receiving 2.5k citations

Hit Papers

Classification and mutation prediction from non–small cel... 2018 2026 2020 2023 2018 500 1000 1.5k
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.

  1. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
    2018 1.7k citations Nicolas Coudray, Paolo Ocampo et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Theodore Sakellaropoulos United States 14 1.1k 994 862 410 393 30 2.5k
Clare Verrill United Kingdom 27 817 0.7× 728 0.7× 761 0.9× 722 1.8× 306 0.8× 102 2.7k
Nina Linder Finland 24 743 0.7× 633 0.6× 490 0.6× 450 1.1× 262 0.7× 57 2.1k
Cleo‐Aron Weis Germany 22 844 0.8× 713 0.7× 375 0.4× 703 1.7× 243 0.6× 71 2.3k
Timo Gaiser Germany 28 837 0.8× 868 0.9× 1.1k 1.3× 1.4k 3.4× 625 1.6× 134 3.7k
António Polónia Portugal 18 977 0.9× 809 0.8× 397 0.5× 366 0.9× 161 0.4× 49 1.7k
Sebastian Foersch Germany 25 478 0.4× 568 0.6× 498 0.6× 767 1.9× 301 0.8× 70 2.2k
Maode Lai China 28 904 0.8× 642 0.6× 873 1.0× 882 2.2× 430 1.1× 77 3.2k
Shidan Wang United States 20 559 0.5× 550 0.6× 313 0.4× 337 0.8× 163 0.4× 45 1.5k
Allen P. Miraflor United States 7 1.3k 1.2× 944 0.9× 180 0.2× 462 1.1× 158 0.4× 11 1.8k
Jia Wu United States 28 437 0.4× 1.4k 1.4× 349 0.4× 404 1.0× 299 0.8× 98 2.4k

Countries citing papers authored by Theodore Sakellaropoulos

Since Specialization
Citations

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

Fields of papers citing papers by Theodore Sakellaropoulos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Theodore Sakellaropoulos

This figure shows the co-authorship network connecting the top 25 collaborators of Theodore Sakellaropoulos. A scholar is included among the top collaborators of Theodore Sakellaropoulos 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 Theodore Sakellaropoulos. Theodore Sakellaropoulos 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.
Do, Catherine, Guimei Jiang, Theodore Sakellaropoulos, et al.. (2025). Binding domain mutations provide insight into CTCF’s relationship with chromatin and its contribution to gene regulation. Cell Genomics. 5(4). 100813–100813. 5 indexed citations
2.
Liu, Bojing, Nicolas Coudray, Theodore Sakellaropoulos, et al.. (2025). Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer. Nature Communications. 16(1). 2328–2328. 2 indexed citations
3.
Hockemeyer, Kathryn, Theodore Sakellaropoulos, Xufeng Chen, et al.. (2024). The stress response regulator HSF1 modulates natural killer cell anti-tumour immunity. Nature Cell Biology. 26(10). 1734–1744. 3 indexed citations
4.
Sakellaropoulos, Theodore, Catherine Do, Guimei Jiang, et al.. (2024). MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data. Nature Communications. 15(1). 6027–6027. 1 indexed citations
5.
Magkrioti, Christiana, Vaia Pliaka, Theodore Sakellaropoulos, et al.. (2022). Lysophosphatidic Acid Is a Proinflammatory Stimulus of Renal Tubular Epithelial Cells. International Journal of Molecular Sciences. 23(13). 7452–7452. 7 indexed citations
6.
Kim, Randie H., Sofia Nomikou, Nicolas Coudray, et al.. (2021). Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas. Journal of Investigative Dermatology. 142(6). 1650–1658.e6. 27 indexed citations
7.
Bernardo-Faura, Martí, Jakob Wirbel, Theodore Sakellaropoulos, et al.. (2021). Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis. Genome Medicine. 13(1). 117–117. 10 indexed citations
8.
Chen, Xufeng, Christina Glytsou, Hua Zhou, et al.. (2019). Targeting Mitochondrial Structure Sensitizes Acute Myeloid Leukemia to Venetoclax Treatment. Cancer Discovery. 9(7). 890–909. 214 indexed citations
9.
Melas, Ioannis N., et al.. (2019). A Computational Platform and Guide for Acceleration of Novel Medicines and Personalized Medicine. Methods in molecular biology. 1939. 181–198. 1 indexed citations
10.
Lhoumaud, Priscillia, Franco Izzo, Theodore Sakellaropoulos, et al.. (2019). EpiMethylTag: simultaneous detection of ATAC-seq or ChIP-seq signals with DNA methylation. Genome biology. 20(1). 248–248. 24 indexed citations
11.
Ocampo, Paolo, André L. Moreira, Nicolas Coudray, et al.. (2018). P1.09-32 Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning. Journal of Thoracic Oncology. 13(10). S562–S562. 13 indexed citations
12.
Sakellaropoulos, Theodore, Asier Antoranz, Cristiano Guttà, et al.. (2018). Phosphoprotein patterns predict trametinib responsiveness and optimal trametinib sensitisation strategies in melanoma. Cell Death and Differentiation. 26(8). 1365–1378. 9 indexed citations
13.
Coudray, Nicolas, Paolo Ocampo, Theodore Sakellaropoulos, et al.. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine. 24(10). 1559–1567. 1738 indexed citations breakdown →
14.
Samara, Pinelopi, Panagiotis Polychronopoulos, Eleni N. Tsakiri, et al.. (2018). Selective cytotoxicity of the herbal substance acteoside against tumor cells and its mechanistic insights. Redox Biology. 16. 169–178. 47 indexed citations
15.
Antoranz, Asier, Theodore Sakellaropoulos, Julio Sáez-Rodríguez, & Leonidas G. Alexopoulos. (2017). Mechanism-based biomarker discovery. Drug Discovery Today. 22(8). 1209–1215. 22 indexed citations
16.
Antoranz, Asier, et al.. (2017). Network-based technologies for early drug discovery. Drug Discovery Today. 23(3). 626–635. 67 indexed citations
17.
Melas, Ioannis N., Theodore Sakellaropoulos, Francesco Iorio, et al.. (2015). Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations
18.
Sakellaropoulos, Theodore, et al.. (2015). Computational Approaches to Accelerating Novel Medicine and Better Patient Care from Bedside to Benchtop. Advances in protein chemistry and structural biology. 102. 147–179. 2 indexed citations
19.
Stavrakas, Vassilis, Ioannis N. Melas, Theodore Sakellaropoulos, & Leonidas G. Alexopoulos. (2015). Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory. PLoS ONE. 10(5). e0128411–e0128411. 3 indexed citations
20.
Poussin, Carine, Carole Mathis, Leonidas G. Alexopoulos, et al.. (2014). The species translation challenge—A systems biology perspective on human and rat bronchial epithelial cells. Scientific Data. 1(1). 140009–140009. 38 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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