Recommending bacterial cultivation conditions from sequence data is a challenging structured problem characterized by missing standardized benchmarks and limited use of sequence structure. To address...
arXiv:2509. 00123v2 Announce Type: replace-cross Abstract: A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions.
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.
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.
Genome-Factory is the first integrated Python library designed to streamline the entire workflow of genomic foundation model development, covering data collection, model tuning, inference, benchmarking, and interpretability. It provides automated pipelines for downloading and preprocessing genomic sequences, supports both full and parameter‑efficient fine‑tuning for diverse models, and offers tools for embedding extraction, DNA sequence generation, and flexible benchmarking. Additionally, the library includes an open‑source biological interpreter based on a sparse auto‑encoder, validated across compatibility, performance, and biological interpretation dimensions.
VenusGenome is a gene‑resolution masked autoencoder that learns microbial genome representations by combining visible‑only encoding with genome‑aware attention, which incorporates contig boundaries, relative strand orientations, and gene positions. The model is pretrained on over 1.7 million microbial genome assemblies using frozen protein embeddings as inputs, and its frozen representations outperform other evaluated models on nine of eleven primary metrics for both genome‑level phenotype prediction and gene‑level tasks. Ablation studies confirm the importance of genome‑aware attention and demonstrate that higher masking ratios and masked autoencoding yield better performance than BERT‑style masked modeling.
arXiv:2509.23552v2 Announce Type: replace-cross Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...
arXiv:2606. 05139v1 Announce Type: new Abstract: The rapid advancement of high-throughput sequencing has led to large, high-dimensional omics datasets.
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:2609.35877v1 Announce Type: cross Abstract: Many DNA foundation models are pretrained by masking parts of a sequence and asking the model to reconstruct them. Standard masked pretraining expose...
arXiv:2508. 07345v2 Announce Type: replace-cross Abstract: \textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages.
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.