Pathogen identification is pivotal in diagnosing, treating, and preventing
diseases, crucial for controlling infections and safeguarding public health.
Traditional alignment-based methods, though widely used, are computationally
intense and reliant on extensive reference databases, often failing to detect
novel pathogens due to their low sensitivity and specificity. Similarly,
conventional machine learning techniques, while promising, require large
annotated datasets and extensive feature engineering and are prone to
overfitting. Addressing these challenges, we introduce PathoLM, a cutting-edge
pathogen language model optimized for the identification of pathogenicity in
bacterial and viral sequences. Leveraging the strengths of pre-trained DNA
models such as the Nucleotide Transformer, PathoLM requires minimal data for
fine-tuning, thereby enhancing pathogen detection capabilities. It effectively
captures a broader genomic context, significantly improving the identification
of novel and divergent pathogens. We developed a comprehensive data set
comprising approximately 30 species of viruses and bacteria, including ESKAPEE
pathogens, seven notably virulent bacterial strains resistant to antibiotics.
Additionally, we curated a species classification dataset centered specifically
on the ESKAPEE group. In comparative assessments, PathoLM dramatically
outperforms existing models like DciPatho, demonstrating robust zero-shot and
few-shot capabilities. Furthermore, we expanded PathoLM-Sp for ESKAPEE species
classification, where it showed superior performance compared to other advanced
deep learning methods, despite the complexities of the task.

通过引入 PathoLM，我们为细菌和病毒序列的致病性鉴定提供了一种优化方法。 PathoLM 可以有效捕捉更广泛的基因组上下文，显著提高了对新颖和多样化病原体的鉴定能力。同时，在 ESKAPEE 物种分类中，PathoLM-Sp 相较于其他深度学习方法展现出卓越的性能。