Michael Heinzinger

6.1k total citations · 4 hit papers
35 papers, 2.5k citations indexed

About

Michael Heinzinger is a scholar working on Molecular Biology, Genetics and Materials Chemistry. According to data from OpenAlex, Michael Heinzinger has authored 35 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 4 papers in Genetics and 4 papers in Materials Chemistry. Recurrent topics in Michael Heinzinger's work include Machine Learning in Bioinformatics (21 papers), Genomics and Phylogenetic Studies (17 papers) and RNA and protein synthesis mechanisms (16 papers). Michael Heinzinger is often cited by papers focused on Machine Learning in Bioinformatics (21 papers), Genomics and Phylogenetic Studies (17 papers) and RNA and protein synthesis mechanisms (16 papers). Michael Heinzinger collaborates with scholars based in Germany, United States and South Korea. Michael Heinzinger's co-authors include Burkhard Rost, Christian Dallago, Ahmed Elnaggar, Yu Wang, Martin Steinegger, Ghalia Rehawi, Christoph Angerer, Tom Gibbs, T. Fehér and Llion Jones and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Michael Heinzinger

34 papers receiving 2.5k 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.

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

2021 1.1k citations Ahmed Elnaggar, Michael Heinzinger et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
Modeling aspects of the language of life through transfer-learning protein sequences

2019 379 citations Michael Heinzinger, Ahmed Elnaggar et al. profile →
Bilingual language model for protein sequence and structure

2024 74 citations Michael Heinzinger, Konstantin Weißenow et al. NAR Genomics and Bioinformatics profile →
Fine-tuning protein language models boosts predictions across diverse tasks

2024 53 citations Michael Heinzinger, Burkhard Rost et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael Heinzinger Germany	19	2.2k	372	206	133	113	35	2.5k
Christian Dallago Germany	14	2.0k 0.9×	337 0.9×	175 0.8×	132 1.0×	119 1.1×	22	2.3k
Roshan Rao United States	6	1.7k 0.7×	346 0.9×	271 1.3×	105 0.8×	121 1.1×	11	2.1k
Robert Verkuil United States	2	1.6k 0.7×	337 0.9×	261 1.3×	100 0.8×	77 0.7×	2	2.0k
Zhongkai Zhu China	1	1.6k 0.7×	336 0.9×	261 1.3×	100 0.8×	77 0.7×	3	2.0k
Mohammed AlQuraishi United States	13	1.4k 0.6×	299 0.8×	285 1.4×	95 0.7×	80 0.7×	24	1.7k
Joshua Meier United States	4	1.3k 0.6×	246 0.7×	125 0.6×	96 0.7×	90 0.8×	7	1.6k
Ahmed Elnaggar Germany	7	1.3k 0.6×	255 0.7×	101 0.5×	49 0.4×	114 1.0×	8	1.5k
Li C. Xue Netherlands	17	1.4k 0.6×	444 1.2×	218 1.1×	83 0.6×	31 0.3×	33	2.0k
Surojit Biswas United States	11	1.7k 0.8×	156 0.4×	127 0.6×	130 1.0×	55 0.5×	15	2.4k
Toshiaki Katayama Japan	17	1.6k 0.7×	167 0.4×	79 0.4×	136 1.0×	63 0.6×	46	2.1k

Countries citing papers authored by Michael Heinzinger

Since Specialization

Citations

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

Fields of papers citing papers by Michael Heinzinger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Heinzinger

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

All Works

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20 of 20 papers shown

Olenyi, Tobias, et al.. (2025). TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins. Journal of Molecular Biology. 437(15). 168997–168997. 2 indexed citations

Heinzinger, Michael & Burkhard Rost. (2024). Artificial Intelligence Learns Protein Prediction. Cold Spring Harbor Perspectives in Biology. 16(9). a041458–a041458. 3 indexed citations

Heinzinger, Michael, et al.. (2024). Protein embeddings predict binding residues in disordered regions. Scientific Reports. 14(1). 13566–13566. 15 indexed citations

Bordin, Nicola, Ian Sillitoe, Michael Heinzinger, et al.. (2023). CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models. Bioinformatics. 39(1). 23 indexed citations

Koludarov, Ivan, Thomas Timm, Carola Greve, et al.. (2023). Prevalent bee venom genes evolved before the aculeate stinger and eusociality. BMC Biology. 21(1). 229–229. 10 indexed citations

Bordin, Nicola, Ian Sillitoe, Clemens Rauer, et al.. (2023). AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms. Communications Biology. 6(1). 160–160. 44 indexed citations

Koludarov, Ivan, Timothy Jackson, Daniel Dashevsky, et al.. (2023). Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily. Nature Communications. 14(1). 4861–4861. 16 indexed citations

Heinzinger, Michael, Maria Littmann, Ian Sillitoe, et al.. (2022). Contrastive learning on protein embeddings enlightens midnight zone. NAR Genomics and Bioinformatics. 4(2). lqac043–lqac043. 60 indexed citations

Bordin, Nicola, Christian Dallago, Michael Heinzinger, et al.. (2022). Novel machine learning approaches revolutionize protein knowledge. Trends in Biochemical Sciences. 48(4). 345–359. 31 indexed citations

10.

Weißenow, Konstantin, Michael Heinzinger, & Burkhard Rost. (2022). Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction. Structure. 30(8). 1169–1177.e4. 78 indexed citations

11.

Olenyi, Tobias, Michael Heinzinger, Michael Bernhofer, et al.. (2022). LambdaPP : Fast and accessible protein‐specific phenotype predictions. Protein Science. 32(1). e4524–e4524. 10 indexed citations

12.

Heinzinger, Michael, et al.. (2022). SETH predicts nuances of residue disorder from protein embeddings. SHILAP Revista de lepidopterología. 2. 1019597–1019597. 29 indexed citations

13.

Heinzinger, Michael, et al.. (2022). Improving protein succinylation sites prediction using embeddings from protein language model. Scientific Reports. 12(1). 16933–16933. 43 indexed citations

14.

Ferruz, Noelia, et al.. (2022). From sequence to function through structure: Deep learning for protein design. Computational and Structural Biotechnology Journal. 21. 238–250. 61 indexed citations

15.

Littmann, Maria, Nicola Bordin, Michael Heinzinger, et al.. (2021). Clustering FunFams using sequence embeddings improves EC purity. Bioinformatics. 37(20). 3449–3455. 15 indexed citations

16.

Stärk, H., Christian Dallago, Michael Heinzinger, & Burkhard Rost. (2021). Light attention predicts protein location from the language of life. Bioinformatics Advances. 1(1). 70 indexed citations

17.

Elnaggar, Ahmed, Michael Heinzinger, Christian Dallago, et al.. (2021). ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(10). 7112–7127. 1057 indexed citations breakdown →

18.

Littmann, Maria, Michael Heinzinger, Christian Dallago, Tobias Olenyi, & Burkhard Rost. (2021). Embeddings from deep learning transfer GO annotations beyond homology. Scientific Reports. 11(1). 1160–1160. 95 indexed citations

19.

Heinzinger, Michael, Tobias Olenyi, Christian Dallago, et al.. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics. 141(10). 1629–1647. 87 indexed citations

20.

Zaucha, Jan, Michael Heinzinger, A. Kulandaisamy, et al.. (2020). Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins. Briefings in Bioinformatics. 22(3). 16 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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