Semi-supervised learning for peptide identification from shotgun proteomics datasets
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- Nature Methods
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doi.org/10.1038/nmeth1113 →Countries where authors are citing Semi-supervised learning for peptide identification from shotgun proteomics datasets
This map shows the geographic impact of Semi-supervised learning for peptide identification from shotgun proteomics datasets. 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 Semi-supervised learning for peptide identification from shotgun proteomics datasets with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Semi-supervised learning for peptide identification from shotgun proteomics datasets more than expected).
Fields of papers citing Semi-supervised learning for peptide identification from shotgun proteomics datasets
This network shows the impact of Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Semi-supervised learning for peptide identification from shotgun proteomics datasets.
About Semi-supervised learning for peptide identification from shotgun proteomics datasets
This paper, published in 2007, received 1.7k indexed citations . Written by Lukas Käll, Jesse D. Canterbury, Jason Weston, William Stafford Noble and Michael J. MacCoss covering the research area of Molecular Biology and Spectroscopy. It is primarily cited by scholars working on Molecular Biology (1.2k citations), Spectroscopy (659 citations) and Immunology (116 citations). Published in Nature Methods.
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.
This paper is also available at doi.org/10.1038/nmeth1113.