Michael Hanselmann

823 citations
13 papers · 612 · h-index 9

Impact in

Papers in

    • Anomaly Detection Techniques and Applications 2
    • Semantic Web and Ontologies 1
    • Mass Spectrometry Techniques and Applications 4
    • Advanced Proteomics Techniques and Applications 2

Michael Hanselmann

13 papers receiving 602 citations

Peers

Michael Hanselmann
Comparison fields: 5 of 100
  • Structural Biology 14
  • Spectroscopy 158
  • Computer Vision and Pattern Recognition 154
  • Polymers and Plastics 81
  • Biophysics 25
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Citations per year

Countries citing papers authored by Michael Hanselmann

Since Specialization
Citations

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

Fields of papers citing papers by Michael Hanselmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Michael Hanselmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Hanselmann Line = papers co-authored together Michael Hanselmann links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2017177
2 2011136
3 2008102
4 200977
5 201129
6
Conditional Flow Variational Autoencoders for Structured Sequence Prediction
201928
7 201625
8 201216
9 201210
10 20127
11 20062
12 20252
13
Fast greedy insertion and deletion in sparse Gaussian process regression.
20151

About Michael Hanselmann

Michael Hanselmann is a scholar working on Artificial Intelligence, Spectroscopy, Molecular Biology, Computer Vision and Pattern Recognition and Analytical Chemistry, having authored 13 papers that have together received 612 indexed citations. Recurring topics across this work include Mass Spectrometry Techniques and Applications (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers), Anomaly Detection Techniques and Applications (2 papers), Spectroscopy and Chemometric Analyses (2 papers), Advanced Proteomics Techniques and Applications (2 papers), Complex Network Analysis Techniques (1 paper), Cancer Genomics and Diagnostics (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Structural Biology (14 citations), Spectroscopy (158 citations), Computer Vision and Pattern Recognition (154 citations), Polymers and Plastics (81 citations) and Biophysics (25 citations). Michael Hanselmann has collaborated with scholars based in Germany, Netherlands and United States. Frequent co-authors include Fred A. Hamprecht, Ron M. A. Heeren, Rainer Stiefelhagen, Attila Reiss, Robert Dürichen, Philip Schmidt, Bernhard Y. Renard, Marc Kirchner, Kristine Glunde and Erika R. Amstalden. Their work appears in journals such as Analytical Chemistry, PLoS ONE, Cardiovascular Diabetology, Journal of Proteome Research and Bioinformatics.

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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