LGGNDec 8, 2025

PlantBiMoE: A Bidirectional Foundation Model with SparseMoE for Plant Genomes

arXiv:2512.07113v1Has CodeBIBM
Originality Incremental advance
AI Analysis

This addresses computational inefficiencies and limited modeling in plant genomics, offering a tool for gene editing and synthetic biology, but it is incremental as it builds on existing methods like Mamba and SparseMoE.

The authors tackled the challenge of modeling plant genomes by proposing PlantBiMoE, a lightweight model that integrates bidirectional Mamba and SparseMoE, achieving best performance on 20 out of 31 datasets in an enhanced benchmark.

Understanding the underlying linguistic rules of plant genomes remains a fundamental challenge in computational biology. Recent advances including AgroNT and PDLLMs have made notable progress although, they suffer from excessive parameter size and limited ability to model the bidirectional nature of DNA strands respectively. To address these limitations, we propose PlantBiMoE, a lightweight and expressive plant genome language model that integrates bidirectional Mamba and a Sparse Mixture-of-Experts (SparseMoE) framework. The bidirectional Mamba enables the model to effectively capture structural dependencies across both the forward and reverse DNA strands, while SparseMoE significantly reduces the number of active parameters, improving computational efficiency without sacrificing modeling capacity. We evaluated and tested our model on the Modified Plants Genome Benchmark (MPGB), an enhanced genomic benchmark, which consolidates 31 datasets across 11 representative tasks, with input sequence lengths ranging from 50 to 6,000 bp. Experimental results demonstrate that PlantBiMoE achieves the best performance on 20 out of 31 datasets and the average best when comparing with existing models. In summary, all above results demonstrate that our model can effectively represent plant genomic sequences, serving as a robust computational tool for diverse genomic tasks, while making substantive contributions to plant genomics, gene editing, and synthetic biology. The code is available at: https://github.com/HUST-Keep-Lin/PlantBiMoE

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