Peter Hönigschmid

2.0k total citations · 1 hit paper
9 papers, 650 citations indexed

About

Peter Hönigschmid is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Peter Hönigschmid has authored 9 papers receiving a total of 650 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 2 papers in Genetics. Recurrent topics in Peter Hönigschmid's work include Machine Learning in Bioinformatics (6 papers), Protein Structure and Dynamics (4 papers) and Bioinformatics and Genomic Networks (3 papers). Peter Hönigschmid is often cited by papers focused on Machine Learning in Bioinformatics (6 papers), Protein Structure and Dynamics (4 papers) and Bioinformatics and Genomic Networks (3 papers). Peter Hönigschmid collaborates with scholars based in Germany, Russia and United States. Peter Hönigschmid's co-authors include Burkhard Rost, Maximilian Hecht, Tobias Hamp, Nir Ben‐Tal, Yana Bromberg, Edda Kloppmann, Gerrit Vriend, Marco Punta, Andrea Schafferhans and Avner Schlessinger and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and BMC Bioinformatics.

In The Last Decade

Peter Hönigschmid

9 papers receiving 645 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.

PredictProtein—an open resource for online prediction of protein structural and functional features

2014 455 citations Guy Yachdav, Edda Kloppmann et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peter Hönigschmid Germany	8	435	83	77	48	48	9	650
Gershon Celniker Israel	7	394 0.9×	93 1.1×	44 0.6×	53 1.1×	39 0.8×	10	583
László Dobson Hungary	13	663 1.5×	76 0.9×	68 0.9×	59 1.2×	71 1.5×	26	862
Tobias Hamp Germany	8	585 1.3×	83 1.0×	93 1.2×	46 1.0×	46 1.0×	10	776
Nikolaos Papandreou Greece	14	591 1.4×	100 1.2×	56 0.7×	42 0.9×	31 0.6×	35	823
István Reményi Hungary	6	384 0.9×	49 0.6×	50 0.6×	44 0.9×	39 0.8×	6	518
Macha Nikolski France	17	617 1.4×	109 1.3×	125 1.6×	54 1.1×	44 0.9×	54	913
Gajinder Pal Singh India	12	449 1.0×	74 0.9×	40 0.5×	31 0.6×	41 0.9×	20	597
Dilmurat Yusuf Germany	12	525 1.2×	60 0.7×	65 0.8×	38 0.8×	24 0.5×	15	793
C. Lachaize Switzerland	4	540 1.2×	64 0.8×	89 1.2×	32 0.7×	38 0.8×	4	699

Countries citing papers authored by Peter Hönigschmid

Since Specialization

Citations

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

Fields of papers citing papers by Peter Hönigschmid

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Hönigschmid

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

All Works

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9 of 9 papers shown

1.

Hönigschmid, Peter, et al.. (2020). AllesTM: predicting multiple structural features of transmembrane proteins. BMC Bioinformatics. 21(1). 242–242. 2 indexed citations

2.

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

3.

Zeng, Bo, Peter Hönigschmid, & Dmitrij Frishman. (2019). Residue co-evolution helps predict interaction sites in α-helical membrane proteins. Journal of Structural Biology. 206(2). 156–169. 15 indexed citations

4.

Kulandaisamy, A., Ramasamy Sakthivel, S. I. Tarnovskaya, et al.. (2018). MutHTP: mutations in human transmembrane proteins. Bioinformatics. 34(13). 2325–2326. 28 indexed citations

5.

Hönigschmid, Peter, et al.. (2018). Evolutionary Interplay between Symbiotic Relationships and Patterns of Signal Peptide Gain and Loss. Genome Biology and Evolution. 10(3). 928–938. 8 indexed citations

6.

Hönigschmid, Peter & Dmitrij Frishman. (2016). Accurate prediction of helix interactions and residue contacts in membrane proteins. Journal of Structural Biology. 194(1). 112–123. 23 indexed citations

7.

Yachdav, Guy, Edda Kloppmann, László Kaján, et al.. (2014). PredictProtein—an open resource for online prediction of protein structural and functional features. Nucleic Acids Research. 42(W1). W337–W343. 455 indexed citations breakdown →

8.

Ivankov, Dmitry N., Natalya S. Bogatyreva, Peter Hönigschmid, et al.. (2013). QARIP: a web server for quantitative proteomic analysis of regulated intramembrane proteolysis. Nucleic Acids Research. 41(W1). W459–W464. 23 indexed citations

9.

Hamp, Tobias, Stefan Seemayer, Esmeralda Vicedo, et al.. (2013). Homology-based inference sets the bar high for protein function prediction. BMC Bioinformatics. 14(S3). S7–S7. 51 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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