{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:11:55Z","timestamp":1776334315943,"version":"3.51.2"},"reference-count":95,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>We present an automated journal recommendation pipeline designed to evaluate the performance of five Sentence Transformer models\u2014all-mpnet-base-v2 (Mpnet), all-MiniLM-L6-v2 (Minilm-l6), all-MiniLM-L12-v2 (Minilm-l12), multi-qa-distilbert-cos-v1 (Multi-qa-distilbert), and all-distilroberta-v1 (roberta)\u2014for recommending journals aligned with a manuscript\u2019s thematic scope. The pipeline extracts domain-relevant keywords from a manuscript via KeyBERT, retrieves potentially related articles from PubMed, and encodes both the test manuscript and retrieved articles into high-dimensional embeddings. By computing cosine similarity, it ranks relevant journals based on thematic overlap. Evaluations on 50 test articles highlight mpnet\u2019s strong performance (mean similarity score 0.71 \u00b1 0.04), albeit with higher computational demands. Minilm-l12 and minilm-l6 offer comparable precision at lower cost, while multi-qa-distilbert and roberta yield broader recommendations better suited to interdisciplinary research. These findings underscore key trade-offs among embedding models and demonstrate how they can provide interpretable, data-driven insights to guide journal selection across varied research contexts.<\/jats:p>","DOI":"10.3390\/bdcc9030067","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T06:53:00Z","timestamp":1741848780000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Comparative Analysis of Sentence Transformer Models for Automated Journal Recommendation Using PubMed Metadata"],"prefix":"10.3390","volume":"9","author":[{"given":"Maria Teresa","family":"Colangelo","sequence":"first","affiliation":[{"name":"Histology and Embryology Laboratory, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43126 Parma, Italy"}]},{"given":"Marco","family":"Meleti","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, Dental School, University of Parma, 43126 Parma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2395-8767","authenticated-orcid":false,"given":"Stefano","family":"Guizzardi","sequence":"additional","affiliation":[{"name":"Histology and Embryology Laboratory, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43126 Parma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8781-1997","authenticated-orcid":false,"given":"Elena","family":"Calciolari","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, Dental School, University of Parma, 43126 Parma, Italy"},{"name":"Centre for Oral Clinical Research, Institute of Dentistry, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7476-7181","authenticated-orcid":false,"given":"Carlo","family":"Galli","sequence":"additional","affiliation":[{"name":"Histology and Embryology Laboratory, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43126 Parma, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"ref_1","first-page":"336","article-title":"Selecting the Right Journal for Your Submission","volume":"4","author":"Welch","year":"2012","journal-title":"J. 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