Maria Littmann

1.4k total citations
15 papers, 483 citations indexed

About

Maria Littmann is a scholar working on Molecular Biology, Epidemiology and Computational Theory and Mathematics. According to data from OpenAlex, Maria Littmann has authored 15 papers receiving a total of 483 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 3 papers in Epidemiology and 2 papers in Computational Theory and Mathematics. Recurrent topics in Maria Littmann's work include Machine Learning in Bioinformatics (8 papers), Genomics and Phylogenetic Studies (7 papers) and Protein Structure and Dynamics (6 papers). Maria Littmann is often cited by papers focused on Machine Learning in Bioinformatics (8 papers), Genomics and Phylogenetic Studies (7 papers) and Protein Structure and Dynamics (6 papers). Maria Littmann collaborates with scholars based in Germany, United Kingdom and United States. Maria Littmann's co-authors include Burkhard Rost, Michael Heinzinger, Christian Dallago, Tobias Olenyi, Nicola Bordin, Christine Orengo, Ian Sillitoe, Konstantin Weißenow, Christine Orengo and Konstantin Schütze and has published in prestigious journals such as Nature Medicine, Bioinformatics and Scientific Reports.

In The Last Decade

Maria Littmann

15 papers receiving 482 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maria Littmann Germany	11	417	56	55	22	22	15	483
Daniel Berenberg United States	4	479 1.1×	118 2.1×	65 1.2×	14 0.6×	17 0.8×	6	582
Xuefeng Cui China	11	294 0.7×	110 2.0×	50 0.9×	21 1.0×	45 2.0×	40	428
Dmitrii Nechaev Germany	3	440 1.1×	73 1.3×	27 0.5×	8 0.4×	26 1.2×	5	471
Elzbieta Rembeza Sweden	6	276 0.7×	31 0.6×	34 0.6×	8 0.4×	23 1.0×	7	354
Guang Qiang Dong Canada	7	304 0.7×	62 1.1×	58 1.1×	13 0.6×	38 1.7×	9	411
Prashant K. Khade United States	10	261 0.6×	79 1.4×	94 1.7×	18 0.8×	38 1.7×	18	435
Khalid Kunji Qatar	8	304 0.7×	63 1.1×	77 1.4×	5 0.2×	36 1.6×	23	396
Mayya Sedova United States	10	333 0.8×	40 0.7×	37 0.7×	13 0.6×	25 1.1×	20	434
Maxwell L. Bileschi United States	7	472 1.1×	42 0.8×	25 0.5×	11 0.5×	71 3.2×	9	593
Jieping Zhao China	3	306 0.7×	33 0.6×	19 0.3×	7 0.3×	26 1.2×	6	374

Countries citing papers authored by Maria Littmann

Since Specialization

Citations

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

Fields of papers citing papers by Maria Littmann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maria Littmann

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Littmann. A scholar is included among the top collaborators of Maria Littmann 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 Maria Littmann. Maria Littmann 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:

15 of 15 papers shown

Littmann, Maria, Max Horn, Andreas Georgiou, et al.. (2025). Varicella-zoster virus reactivation and the risk of dementia. Nature Medicine. 31(12). 4172–4179. 3 indexed citations

Schwab, Patrick, Maria Littmann, Carolyn Buser‐Doepner, et al.. (2024). Recombinant zoster vaccine and reduced risk of dementia: matched‐cohort study using large‐scale electronic health records and machine learning methodology. Alzheimer s & Dementia. 20(S7). 2 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

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

Weis, Caroline, Maria Littmann, Dickens Theodore, et al.. (2023). Deep learning cluster analysis reveals subtypes in response to antisense oligonucleotide therapy in chronic hepatitis B. Journal of Hepatology. 78. S1151–S1152. 1 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

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

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

10.

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

11.

Littmann, Maria, Michael Heinzinger, Christian Dallago, Konstantin Weißenow, & Burkhard Rost. (2021). Protein embeddings and deep learning predict binding residues for various ligand classes. Scientific Reports. 11(1). 23916–23916. 72 indexed citations

12.

Dallago, Christian, Konstantin Schütze, Michael Heinzinger, et al.. (2021). Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets. Current Protocols. 1(5). e113–e113. 59 indexed citations

13.

Littmann, Maria, Liel Cohen-Lavi, Yotam Frank, et al.. (2020). Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nature Machine Intelligence. 2(1). 18–24. 45 indexed citations

14.

Littmann, Maria, Tatyana Goldberg, Sebastian Seitz, Mikael Bodén, & Burkhard Rost. (2019). Detailed prediction of protein sub-nuclear localization. BMC Bioinformatics. 20(1). 205–205. 6 indexed citations

15.

Littmann, Maria, et al.. (2019). FunFam protein families improve residue level molecular function prediction. BMC Bioinformatics. 20(1). 400–400. 17 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.

Explore authors with similar magnitude of impact