Plant biosynthetic gene clusters (BGCs) encode specialized-metabolite pathways, yet curated plant BGC labels remain scarce, hindering supervised discovery at genome scale. Existing plant BGC mining tools are largely signature- and rule-driven and do not fully leverage recent advances in contextual representation learning for modeling long-range domain context and controlling false positives under strong domain shift.
arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.
By Hyunjin Seo, Hyeon Hwang, Gyubok Lee, Jay Shin, Jimin Park, Taesoo Kim, Sanghoon Lee, Hongjoon Ahn, Sungjun Han, Sangwon Jung
arXiv:2607. 06224v1 Announce Type: new Abstract: Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering.
By Jake Bowden, Laurence Legon, Satnam Surae
arXiv:2608. 08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction.
By Alejandro L. Garc\'ia-Navarro, Carlos Sevilla-Salcedo, Bel\'en Rodr\'iguez-S\'anchez, Vanessa G\'omez-Verdejo
arXiv:2608. 06727v1 Announce Type: new Abstract: Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation.
By Koushik Howlader, Tirtho Roy, Md Tauhidul Islam, Wei Le
arXiv:2607. 19426v2 Announce Type: replace-cross Abstract: Large single-cell datasets are expensive to store, curate, and repeatedly reuse for model training.
By Yaodi Luo, Peize He, Lingbei Meng, Bowen Han, Zheng Lu, Jianqing Zhu, Lian Zhang