arXiv AI November 5, 2026 By Zhang Liang, Zhang Yukun, Tan Yang, Jiang Fan, Zhong Bozitao, Cheng Xiaoran, Hong Liang, Li Mingchen, Yu Yuanxi

VenusGenome: Genome-Aware Masked Autoencoding for Microbial Genome Representation Learning

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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.

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arXiv Machine Learning

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rag fine-tuning benchmarks safety

arXiv AI

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JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

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arXiv Machine Learning

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Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

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By Jeffrey D. Varner

llms safety

arXiv Machine Learning

Jul 31

PlantBGC: Transformer for Plant BGC Discovery via Label-Free Domain Adaptation and Weak Supervision

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.

By Yuhan Zhao, Nidhi Grover, Zhishan Guo, Ning Sui

llms fine-tuning benchmarks

Hugging Face Trending Papers

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PlantBGC: Transformer for Plant BGC Discovery via Label-Free Domain Adaptation and Weak Supervision

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.

llms fine-tuning benchmarks

arXiv Machine Learning

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GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for \textit{De Novo} Peptide Sequencing

GyroNovo is a new framework for de novo peptide sequencing that improves fragment imputation by guiding the process with decoder errors observed during training. It introduces mass-aware attention using rotary embeddings to encode pairwise mass differences between spectral peaks, and creates easy and hard augmented views of spectra to train the decoder under varying corruption levels. Experiments on NovoBench demonstrate significant gains, with about 9 percentage points higher peptide-level precision and 7 percentage points higher amino-acid-level precision compared to the state-of-the-art baseline.

By Abdellah El Mekki, Laks V. S. Lakshmanan, Muhammad Abdul-Mageed

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Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

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PlantBGC: Transformer for Plant BGC Discovery via Label-Free Domain Adaptation and Weak Supervision

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for \textit{De Novo} Peptide Sequencing