Andreas Mayr

3.6k total citations · 2 hit papers
17 papers, 1.9k citations indexed

About

Andreas Mayr is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Andreas Mayr has authored 17 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Computational Theory and Mathematics and 4 papers in Genetics. Recurrent topics in Andreas Mayr's work include Computational Drug Discovery Methods (6 papers), Gene expression and cancer classification (6 papers) and Machine Learning in Materials Science (4 papers). Andreas Mayr is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Gene expression and cancer classification (6 papers) and Machine Learning in Materials Science (4 papers). Andreas Mayr collaborates with scholars based in Austria, Belgium and United States. Andreas Mayr's co-authors include Sepp Hochreiter, Günter Klambauer, Thomas Unterthiner, Djork-Arné Clevert, Andreas Mitterecker, Ulrich Bodenhofer, Hugo Ceulemans, Jörg K. Wegner, Karin Schwarzbauer and Marvin Steijaert and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Scientific Reports.

In The Last Decade

Andreas Mayr

16 papers receiving 1.9k citations

Hit Papers

align trajectories

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.

DeepTox: Toxicity Prediction using Deep Learning

2016 638 citations Andreas Mayr, Günter Klambauer et al. Frontiers in Environmental Science profile →
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

2018 352 citations Andreas Mayr, Günter Klambauer et al. Chemical Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andreas Mayr Austria	13	1.0k	879	444	262	258	17	1.9k
Djork-Arné Clevert Germany	18	1.5k 1.5×	1.1k 1.3×	765 1.7×	197 0.8×	299 1.2×	38	2.4k
Günter Klambauer Austria	24	1.5k 1.5×	1.6k 1.8×	758 1.7×	308 1.2×	287 1.1×	45	3.1k
Yi Xiong China	32	2.2k 2.2×	807 0.9×	249 0.6×	232 0.9×	62 0.2×	126	3.1k
Hilal Tayara South Korea	31	1.9k 1.9×	468 0.5×	187 0.4×	211 0.8×	60 0.2×	133	2.8k
Ina Koch Germany	26	2.1k 2.1×	325 0.4×	143 0.3×	181 0.7×	133 0.5×	107	3.3k
Emma J. Chory United States	11	1.4k 1.4×	513 0.6×	300 0.7×	133 0.5×	107 0.4×	15	2.2k
Juan Liu China	26	1.2k 1.2×	305 0.3×	123 0.3×	433 1.7×	69 0.3×	168	2.2k
Ling‐Yun Wu China	29	2.4k 2.4×	471 0.5×	95 0.2×	252 1.0×	154 0.6×	94	3.6k
Abdollah Dehzangi United States	32	2.6k 2.6×	472 0.5×	208 0.5×	367 1.4×	34 0.1×	102	3.5k
Claus A. Andersen Denmark	11	1.3k 1.3×	302 0.3×	132 0.3×	304 1.2×	92 0.4×	16	2.2k

Countries citing papers authored by Andreas Mayr

Since Specialization

Citations

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

Fields of papers citing papers by Andreas Mayr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andreas Mayr

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

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

Brandstetter, J., et al.. (2025). VN-EGNN: E(3)- and SE(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification. Journal of Cheminformatics. 18(1). 11–11.

Mayr, Andreas, et al.. (2024). Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions. Scientific Reports. 14(1). 26802–26802. 9 indexed citations

Sturm, Noé, Andreas Mayr, Vladimir Chupakhin, et al.. (2020). Industry-scale application and evaluation of deep learning for drug target prediction. Journal of Cheminformatics. 12(1). 26–26. 29 indexed citations

Sturm, Noé, Jiangming Sun, Andreas Mayr, et al.. (2018). Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. Journal of Chemical Information and Modeling. 59(3). 962–972. 28 indexed citations

Mayr, Andreas, Günter Klambauer, Thomas Unterthiner, et al.. (2018). Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chemical Science. 9(24). 5441–5451. 352 indexed citations breakdown →

Klambauer, Günter, Thomas Unterthiner, Andreas Mayr, & Sepp Hochreiter. (2017). DeepTox: Toxicity prediction using deep learning. Toxicology Letters. 280. S69–S69. 30 indexed citations

Arjona-Medina, Jose A., Thomas Unterthiner, Rupesh Durgesh, et al.. (2016). Speeding up Semantic Segmentation for Autonomous Driving. 165 indexed citations

Mayr, Andreas, Günter Klambauer, Thomas Unterthiner, & Sepp Hochreiter. (2016). DeepTox: Toxicity Prediction using Deep Learning. Frontiers in Environmental Science. 3. 638 indexed citations breakdown →

Clevert, Djork-Arné, Andreas Mayr, Thomas Unterthiner, & Sepp Hochreiter. (2015). Rectified factor networks. Neural Information Processing Systems. 28. 1855–1863. 2 indexed citations

10.

Klambauer, Günter, et al.. (2015). Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map. Bioinformatics. 31(20). 3392–3394. 15 indexed citations

11.

Streit, Marc, et al.. (2014). Furby: fuzzy force-directed bicluster visualization. BMC Bioinformatics. 15(S6). S4–S4. 38 indexed citations

12.

Clevert, Djork-Arné, Andreas Mayr, Andreas Mitterecker, et al.. (2013). Increasing the discovery power of -omics studies. 1(2). 84–93. 1 indexed citations

13.

Klambauer, Günter, Karin Schwarzbauer, Andreas Mayr, et al.. (2012). cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Research. 40(9). e69–e69. 312 indexed citations

14.

Clevert, Djork-Arné, Andreas Mitterecker, Andreas Mayr, et al.. (2011). cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate. Nucleic Acids Research. 39(12). e79–e79. 19 indexed citations

15.

Hochreiter, Sepp, Ulrich Bodenhofer, Martin Heusel, et al.. (2010). FABIA: factor analysis for bicluster acquisition. Bioinformatics. 26(12). 1520–1527. 206 indexed citations

16.

Hochreiter, Sepp, Ulrich Bodenhofer, Martin Heusel, et al.. (2010). FABIA: factor analysis for bicluster acquisition. 1. 57 indexed citations

17.

Mayr, Andreas, et al.. (2006). Comparison of Hematologic Data in World Elite Junior Speed Skaters and in Non-Athletic Juniors. International Journal of Sports Medicine. 27(4). 283–288. 15 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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