Jannis Born

1.6k total citations
25 papers, 744 citations indexed

About

Jannis Born is a scholar working on Computational Theory and Mathematics, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jannis Born has authored 25 papers receiving a total of 744 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 11 papers in Molecular Biology and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jannis Born's work include Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (8 papers) and COVID-19 diagnosis using AI (7 papers). Jannis Born is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (8 papers) and COVID-19 diagnosis using AI (7 papers). Jannis Born collaborates with scholars based in Switzerland, Germany and United States. Jannis Born's co-authors include Matteo Manica, María Rodríguez Martínez, Ali Oskooei, Anna Weber, Julio Sáez-Rodríguez, Vigneshwari Subramanian, Avinash Aujayeb, Nina Wiedemann, Asim Khan and Anwaar Ulhaq and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Jannis Born

23 papers receiving 726 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jannis Born Switzerland 15 316 272 243 166 133 25 744
Coryandar Gilvary United States 5 264 0.8× 211 0.8× 210 0.9× 107 0.6× 183 1.4× 5 757
Bonggun Shin United States 10 185 0.6× 233 0.9× 154 0.6× 33 0.2× 212 1.6× 14 763
Ladislav Rampášek Canada 6 257 0.8× 153 0.6× 50 0.2× 41 0.2× 71 0.5× 8 532
Elyas Sabeti United States 8 227 0.7× 258 0.9× 36 0.1× 78 0.5× 66 0.5× 16 424
Peiran Jiang China 9 369 1.2× 124 0.5× 114 0.5× 40 0.2× 76 0.6× 12 583
Maha A. Thafar Saudi Arabia 12 465 1.5× 297 1.1× 61 0.3× 87 0.5× 73 0.5× 29 700
Tianfan Fu United States 11 368 1.2× 380 1.4× 29 0.1× 198 1.2× 251 1.9× 37 812
Vladimir Aladinskiy Russia 9 360 1.1× 431 1.6× 56 0.2× 301 1.8× 38 0.3× 17 764
Daniil Polykovskiy Russia 8 274 0.9× 327 1.2× 28 0.1× 221 1.3× 60 0.5× 14 562

Countries citing papers authored by Jannis Born

Since Specialization
Citations

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

Fields of papers citing papers by Jannis Born

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jannis Born

This figure shows the co-authorship network connecting the top 25 collaborators of Jannis Born. A scholar is included among the top collaborators of Jannis Born 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 Jannis Born. Jannis Born 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.
Wiedemann, Nina, Dianne de Korte‐de Boer, Michael Richter, et al.. (2025). COVID-BLUeS - a Prospective Study on the Value of AI in Lung Ultrasound Analysis. IEEE Journal of Biomedical and Health Informatics. 29(9). 6301–6310.
2.
Zipoli, Federico, Carlo Baldassari, Matteo Manica, Jannis Born, & Teodoro Laino. (2024). Growing strings in a chemical reaction space for searching retrosynthesis pathways. npj Computational Materials. 10(1). 5 indexed citations
3.
Ovchinnikova, Katja, Jannis Born, Panagiotis Chouvardas, Maria Anna Rapsomaniki, & Marianna Kruithof‐de Julio. (2024). Overcoming limitations in current measures of drug response may enable AI-driven precision oncology. npj Precision Oncology. 8(1). 95–95. 9 indexed citations
4.
Born, Jannis, et al.. (2023). Chemical representation learning for toxicity prediction. Digital Discovery. 2(3). 674–691. 21 indexed citations
5.
Born, Jannis & Matteo Manica. (2023). Regression Transformer enables concurrent sequence regression and generation for molecular language modelling. Nature Machine Intelligence. 5(4). 432–444. 72 indexed citations
6.
Park, Nathaniel H., Matteo Manica, Jannis Born, et al.. (2023). Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language. Nature Communications. 14(1). 3686–3686. 19 indexed citations
7.
Born, Jannis, et al.. (2022). A computational investigation of inventive spelling and the “Lesen durch Schreiben” method. Computers and Education Artificial Intelligence. 3. 100063–100063. 1 indexed citations
8.
Born, Jannis, David Beymer, Deepta Rajan, et al.. (2021). On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2(6). 100269–100269. 38 indexed citations
9.
Born, Jannis, David Beymer, Deepta Rajan, et al.. (2021). On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2(8). 100330–100330. 11 indexed citations
10.
Weber, Anna, Jannis Born, & María Rodríguez Martínez. (2021). TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics. 37(Supplement_1). i237–i244. 95 indexed citations
11.
Born, Jannis, et al.. (2021). Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model. Journal of Chemical Information and Modeling. 62(2). 240–257. 17 indexed citations
12.
Born, Jannis, Nina Wiedemann, Julie Goulet, et al.. (2021). Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. Applied Sciences. 11(2). 672–672. 96 indexed citations
13.
14.
Born, Jannis, et al.. (2021). PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning. iScience. 24(4). 102269–102269. 55 indexed citations
15.
Born, Jannis & Matteo Manica. (2021). Trends in Deep Learning for Property-driven Drug Design. Current Medicinal Chemistry. 28(38). 7862–7886. 16 indexed citations
16.
Chenthamarakshan, Vijil, Payel Das, Samuel C. Hoffman, et al.. (2020). CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. neural information processing systems. 33. 4320–4332. 30 indexed citations
17.
Born, Jannis, et al.. (2020). Chemical representation learning for toxicity prediction. Faculty of 1000 Research Ltd. 9. 3 indexed citations
18.
Ulhaq, Anwaar, Jannis Born, Asim Khan, et al.. (2020). COVID-19 Control by Computer Vision Approaches: A Survey. Repository for Publications and Research Data (ETH Zurich). 62 indexed citations
19.
Manica, Matteo, Ali Oskooei, Jannis Born, et al.. (2019). Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics. 16(12). 4797–4806. 91 indexed citations
20.
Born, Jannis, et al.. (2017). Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system. PLoS ONE. 12(5). e0178304–e0178304. 4 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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