{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T14:09:42Z","timestamp":1761746982693,"version":"build-2065373602"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":59,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Soil probiotics are indispensable in agro-ecosystems, enhancing crop yield through nutrient solubilization, pathogen suppression, and soil structure improvement. However, reliable prediction methods for soil probiotics are still lacking. In this study, we use genomic foundation models to generate representations from sample sequences and enhance them by deeply integrating domain-specific engineered features. The enhanced representations enable training a powerful classifier for a target task, rather than relying on conventional parameter fine-tuning. Inspired by the stacking ensemble learning framework, we design a stacked aggregation classifier. It predicts a sample\u2019s label by leveraging only a subset of its sequence segments, effectively addressing the challenges in processing long or incompletely assembled sequences. The proposed method is applied to the prediction of soil probiotics and demonstrates excellent performance on both balanced and imbalanced test sets. Furthermore, potential functional genes are revealed from the predicted probiotics, providing valuable biological insights for related studies.<\/jats:p>","DOI":"10.1093\/bib\/bbaf567","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T11:36:39Z","timestamp":1759750599000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction of soil probiotics based on foundation model representation enhancement and stacked aggregation classifier"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6579-7944","authenticated-orcid":false,"given":"Qiang","family":"Kang","sequence":"first","affiliation":[{"name":"BGI Research , No. 59, Keji 3rd Road, Jiangxia District, Wuhan 430074 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotong","family":"Sun","sequence":"additional","affiliation":[{"name":"BGI Research , No. 59, Keji 3rd Road, Jiangxia District, Wuhan 430074 ,","place":["China"]},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences , No. 19, Yuquan Road, Shijingshan District, Beijing 100049 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yayu","family":"Wang","sequence":"additional","affiliation":[{"name":"BGI Research , No. 9, Yunhua Road, Yantian District, Shenzhen 518003 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Fang","sequence":"additional","affiliation":[{"name":"BGI Research , No. 9, Yunhua Road, Yantian District, Shenzhen 518003 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1575-3692","authenticated-orcid":false,"given":"Yuxiang","family":"Li","sequence":"additional","affiliation":[{"name":"BGI Research , No. 59, Keji 3rd Road, Jiangxia District, Wuhan 430074 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