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. 27258v1 Announce Type: cross Abstract: Plant biosynthetic gene clusters (BGCs) encode specialized-metabolite pathways, yet curated plant BGC labels remain scarce, hindering supervised discovery at genome scale.
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.
arXiv:2605.28868v2 Announce Type: replace-cross Abstract: Metagenomic taxonomic annotation is essential for interpreting complex microbial communities, yet reliable annotation remains challenging und...
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.
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.
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.
arXiv:2607. 19426v2 Announce Type: replace-cross Abstract: Large single-cell datasets are expensive to store, curate, and repeatedly reuse for model training.
Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation. Mixture-of-Recursions (MoR) improves efficiency through adaptive token-choice or expert-choice routing.
PlantC2USeg is a deep transfer‑learning framework that uses cross‑scale consistency learning and an information‑restricted decoder to improve plant point cloud segmentation. It achieves state‑of‑the‑art performance on Soybean3D and ShapeNet Part, and demonstrates strong few‑shot generalization across species and sensing conditions. The method reduces the need for large annotated datasets and lowers adaptation overhead for new plant species.
arXiv:2609.24538v1 Announce Type: new Abstract: Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far f...
arXiv:2609.37675v1 Announce Type: new Abstract: Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and str...
HERBIOME is a modular, end‑to‑end pipeline that automates the digitization of herbarium labels. It combines YOLOv8 for component detection, CRAFT Hezar for word‑level text localization, a fine‑tuned TrOCR model for mixed handwritten and printed text recognition, and GPT‑4o Mini for structuring metadata into standardized fields. Evaluation on 450 French specimens shows high surface similarity (MWS ≈ 0.616) and moderate semantic accuracy (SMA ≈ 0.442), with taxonomic fields identified as the main challenge.