{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T06:22:59Z","timestamp":1773469379614,"version":"3.50.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Provincial Department of Education Young Talent Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Citation is central to scholarly communication, enabling researchers to navigate rapidly expanding literature and identify relevant prior work. Yet the \u2018reasoning\u2019 behind why a particular paper is cited is often implicit or opaque. Although academic search engines and literature tools rank candidate papers for a query, the motivations underlying these rankings are rarely transparent, making it difficult for scholars to interpret and act on retrieved results\u2014especially in biomedical research where domain knowledge is essential.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose an encoder\u2013decoder framework that leverages curated biomedical knowledge to generate \u2018explanations of citation motivation\u2019 in a structured bio-triplet format. We evaluate the approach against recent families of pre-trained language models for text generation, including BERT-style (and variants) and GPT-style (and variants) models. In cancer-focused experiments using PubMed Central, we annotate over 10\u2009000 citation relations with bio-triplets grounded in curated knowledge from multiple biomedical databases. Trained on these annotations, our model outperforms strong sequence-generation baselines, improving precision, recall, and F1 for citation-motivation generation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code and data are available at Zenodo (archival DOI: 10.281\/zenodo.14893445) and GitHub: https:\/\/github.com\/zhongxiangboy\/Knowledge-based-Citation-Reasoning-for-Biomedical-Domain.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag061","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T19:18:19Z","timestamp":1771960699000},"source":"Crossref","is-referenced-by-count":0,"title":["Knowledge-based citation reasoning for biomedical domain"],"prefix":"10.1093","volume":"42","author":[{"given":"Pengcheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Economics and Management, Hubei University of Technology , Wuhan 430068, Hubei,","place":["China"]},{"name":"Hubei Digital Industrial Economic Development Research Center, Hubei University of Technology , Wuhan 430068, Hubei,","place":["China"]}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science and Data Science, Worcester Polytechnic Institute , Worcester, MA 01609,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-8323","authenticated-orcid":false,"given":"Xiaozhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Science and Data Science, Worcester Polytechnic Institute , Worcester, MA 01609,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7563-9915","authenticated-orcid":false,"given":"Xuhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University , 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