{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T12:53:05Z","timestamp":1791204785795,"version":"4.2.0"},"reference-count":72,"publisher":"American Association for the Advancement of Science (AAAS)","content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Health Data Sci"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-based models, this trade-off has not been systematically studied across biomedical literature and clinical text.\n                    <jats:bold>Methods:<\/jats:bold>\n                    We developed BioMamba, a family of biomedical Mamba2 models at 5 scales (130M, 370M, 780M, 1.3B, and 2.7B) obtained by continued pretraining of released public Mamba2 checkpoints on a balanced 80%\/10%\/10% mixture of PubMed abstracts, the Colossal Clean Crawled Corpus (C4), and Wikipedia. The architecture is unchanged from Mamba2; the contribution is the adaptation recipe and the accompanying open-weight checkpoints. We evaluated internal language modeling on fixed held-out sets, out-of-domain multiple-choice benchmarks, and 3 downstream tasks across multiple model scales: clinical note completion and discharge summary generation on MIMIC-IV-Note, and biomedical yes\/no question answering on Biomedical Semantic Indexing and Question Answering (BioASQ) and PubMed Question Answering (PubMedQA).\n                    <jats:bold>Results:<\/jats:bold>\n                    Across 5 scales, BioMamba consistently lowered PubMed perplexity, improved Wikipedia-style held-out perplexity by 1.46 to 4.72 PPL, and left C4 perplexity essentially unchanged (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mo>\u2223<\/mml:mo>\n                        <mml:mspace width=\"-0.2em\"\/>\n                        <mml:mi>\u0394<\/mml:mi>\n                        <mml:mi>P<\/mml:mi>\n                        <mml:mi>P<\/mml:mi>\n                        <mml:mi>L<\/mml:mi>\n                        <mml:mspace width=\"-0.2em\"\/>\n                        <mml:mo>\u2223<\/mml:mo>\n                        <mml:mspace width=\"-0.2em\"\/>\n                        <mml:mo>\u2264<\/mml:mo>\n                        <mml:mn>0<\/mml:mn>\n                        <mml:mo>.17<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ). On 6 out-of-domain multiple-choice benchmarks, BioMamba stayed within \u00b13 pp of Mamba2 with no systematic regression. After supervised fine-tuning, BioMamba+SFT matched or exceeded Mamba2+SFT on MIMIC-IV note completion and discharge summary generation at every evaluated scale (paired-bootstrap\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mi>P<\/mml:mi>\n                        <mml:mfenced>\n                          <mml:mrow>\n                            <mml:mi>\u0394<\/mml:mi>\n                            <mml:mo>&gt;<\/mml:mo>\n                            <mml:mn>0<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:mfenced>\n                        <mml:mo>\u2265<\/mml:mo>\n                        <mml:mn>0<\/mml:mn>\n                        <mml:mo>.997<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    at 2.7B) and improved PubMedQA at every scale. The strongest model (BioMamba-2.7B) reached a PubMed perplexity of 5.28 and accuracies of 90.24% and 73.00% on BioASQ and PubMedQA, respectively.\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    A balanced domain-adaptive continued pretraining recipe strengthens Mamba2 language models on biomedical literature and clinical text while preserving general-language-modeling fluency.\n                  <\/jats:p>","DOI":"10.34133\/hds.0454","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:57:17Z","timestamp":1778590637000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["BioMamba: Domain-Adaptive Biomedical Language Models"],"prefix":"10.34133","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9130-5962","authenticated-orcid":false,"given":"Ling","family":"Yue","sequence":"first","affiliation":[{"name":"Department of Computer Science, \rRensselaer Polytechnic Institute, Troy, NY, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2086-6715","authenticated-orcid":false,"given":"Mingzhi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, \rRensselaer Polytechnic Institute, Troy, NY, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0308-2486","authenticated-orcid":false,"given":"Sixue","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Computer Science, \rRensselaer Polytechnic Institute, Troy, NY, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6452-5779","authenticated-orcid":true,"given":"Yunning","family":"Cao","sequence":"additional","affiliation":[{"name":"Jiaxing New Jies Thermal Power Co. Ltd.","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7997-434X","authenticated-orcid":true,"given":"Yanbo","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Data Science and Technology, \rNorth University of China, Taiyuan, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7830-5777","authenticated-orcid":false,"given":"Vijil","family":"Chenthamarakshan","sequence":"additional","affiliation":[{"name":"IBM Research","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2462-362X","authenticated-orcid":false,"given":"Shaowu","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Aerospace, and Nuclear Engineering, \rRensselaer Polytechnic Institute, Troy, NY, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7288-0516","authenticated-orcid":true,"given":"Payel","family":"Das","sequence":"additional","affiliation":[{"name":"IBM Research","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianfan","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, \rNanjing University, Nanjing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"issue":"2","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3490238","article-title":"Biomedical question answering: A survey of approaches and challenges","volume":"55","author":"Jin Q","year":"2021","unstructured":"Jin Q, Yuan Z, Xiong G, Yu Q, Ying H, Tan C, Chen M, Huang S, Liu X, Yu S. 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