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However, existing machine learning methods have not considered relationship among the proteins sufficiently.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein\u2013protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04022-w","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T07:03:56Z","timestamp":1622617436000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["PIKE-R2P: Protein\u2013protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction"],"prefix":"10.1186","volume":"22","author":[{"given":"Xinnan","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shike","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piyushkumar A.","family":"Mundra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6774-9786","authenticated-orcid":false,"given":"Jie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"issue":"5","key":"4022_CR1","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.3390\/cells9051130","volume":"9","author":"JR Choi","year":"2020","unstructured":"Choi JR, Yong KW, Choi JY, Cowie AC. 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