Improved methods for predicting peptide binding affinity to MHC class II molecules

530 indexed citations

Abstract

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About

This paper, published in 2018, received 530 indexed citations. Written by Kamilla Kjærgaard Jensen, Massimo Andreatta, Paolo Marcatili, Søren Buus, Jason Greenbaum, Yan Zhen, Alessandro Sette, Bjoern Peters and Morten Nielsen covering the research area of Molecular Biology, Immunology and Radiology, Nuclear Medicine and Imaging. It is primarily cited by scholars working on Molecular Biology (422 citations), Immunology (252 citations) and Radiology, Nuclear Medicine and Imaging (188 citations). Published in Immunology.

Countries where authors are citing Improved methods for predicting peptide binding affinity to MHC class II molecules

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This map shows the geographic impact of Improved methods for predicting peptide binding affinity to MHC class II molecules. 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 Improved methods for predicting peptide binding affinity to MHC class II molecules with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Improved methods for predicting peptide binding affinity to MHC class II molecules more than expected).

Fields of papers citing Improved methods for predicting peptide binding affinity to MHC class II molecules

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Improved methods for predicting peptide binding affinity to MHC class II molecules. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Improved methods for predicting peptide binding affinity to MHC class II molecules.

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/10.1111/imm.12889.

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