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。

PathoLM: 通过基因组基础模型从 DNA 序列识别致病性

PathoLM: Identifying pathogenicity from the DNA sequence through the  Genome Foundation Model

Long traces and large event logs that originate from sensors and prediction
models are becoming more common in our data-rich world. In such circumstances,
conformance checking, a key task in process mining, can become computationally
infeasible due to the exponential complexity of finding an optimal alignment.
This paper introduces a novel sliding window approach to address these
scalability challenges while preserving the interpretability of alignment-based
methods. By breaking down traces into manageable subtraces and iteratively
aligning each with the process model, our method significantly reduces the
search space.
The approach uses global information that captures structural properties of
the trace and the process model to make informed alignment decisions,
discarding unpromising alignments even if they are optimal for a local
subtrace. This improves the overall accuracy of the results.
Experimental evaluations demonstrate that the proposed method consistently
finds optimal alignments in most cases and highlight its scalability. This is
further supported by a theoretical complexity analysis, which shows the reduced
growth of the search space compared to other common conformance checking
methods.
This work provides a valuable contribution towards efficient conformance
checking for large-scale process mining applications.

本研究提出了一种新颖的滑动窗口方法，以解决大规模过程挖掘应用中的符合性检查问题，并在保持基于对齐方法的可解释性的同时，显著降低搜索空间。该方法利用捕获跟踪信息和过程模型的结构特性的全局信息，做出明智的对齐决策，从而提高了结果的整体准确性。实验评估结果显示该方法在大多数情况下能够稳定地找到最优对齐，并展示了其可扩展性。理论复杂性分析进一步表明与其他常见的符合性检查方法相比，该方法搜索空间的增长较小。这项研究为大规模过程挖掘应用的高效符合性检查提供了宝贵的贡献。