During times of increasing antibiotic resistance and the spread of infectious
diseases like COVID-19, it is important to classify genes related to antibiotic
resistance. As natural language processing has advanced with transformer-based
language models, many language models that learn characteristics of nucleotide
sequences have also emerged. These models show good performance in classifying
various features of nucleotide sequences. When classifying nucleotide
sequences, not only the sequence itself, but also various background knowledge
is utilized. In this study, we use not only a nucleotide sequence-based
language model but also a text language model based on PubMed articles to
reflect more biological background knowledge in the model. We propose a method
to fine-tune the nucleotide sequence language model and the text language model
based on various databases of antibiotic resistance genes. We also propose an
LLM-based augmentation technique to supplement the data and an ensemble method
to effectively combine the two models. We also propose a benchmark for
evaluating the model. Our method achieved better performance than the
nucleotide sequence language model in the drug resistance class prediction.

通过使用核苷酸序列语言模型和基于 PubMed 文章的文本语言模型，我们提出了一种细调核苷酸序列语言模型和文本语言模型的方法，以及一种 LLM 基础上的数据增强技术和有效结合两个模型的集成方法。我们还提出了一种用于评估模型的基准，在药物抗性类别预测中，我们的方法表现出比核苷酸序列语言模型更好的性能。

利用大型语言模型预测抗菌性能

Predicting Anti-microbial Resistance using Large Language Models

Clinicians prescribe antibiotics by looking at the patient's health record
with an experienced eye. However, the therapy might be rendered futile if the
patient has drug resistance. Determining drug resistance requires
time-consuming laboratory-level testing while applying clinicians' heuristics
in an automated way is difficult due to the categorical or binary medical
events that constitute health records. In this paper, we propose a novel
framework for rapid clinical intervention by viewing health records as graphs
whose nodes are mapped from medical events and edges as correspondence between
events in given a time window. A novel graph-based model is then proposed to
extract informative features and yield automated drug resistance analysis from
those high-dimensional and scarce graphs. The proposed method integrates
multi-task learning into a common feature extracting graph encoder for
simultaneous analyses of multiple drugs as well as stabilizing learning. On a
massive dataset comprising over 110,000 patients with urinary tract infections,
we verify the proposed method is capable of attaining superior performance on
the drug resistance prediction problem. Furthermore, automated drug
recommendations resemblant to laboratory-level testing can also be made based
on the model resistance analysis.

采用基于图的模型从医疗事件中提取信息特征，可预测药物耐药性并自动进行类似实验室级别的药物推荐。

基于多任务图表示的罕见健康记录药物耐药性分析

Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation

A new statistical procedure (Model-X \cite{candes2018}) has provided a way to
identify important factors using any supervised learning method controlling for
FDR. This line of research has shown great potential to expand the horizon of
machine learning methods beyond the task of prediction, to serve the broader
needs in scientific researches for interpretable findings. However, the lack of
a practical and flexible method to generate knockoffs remains the major
obstacle for wide application of Model-X procedure. This paper fills in the gap
by proposing a model-free knockoff generator which approximates the correlation
structure between features through latent variable representation. We
demonstrate our proposed method can achieve FDR control and better power than
two existing methods in various simulated settings and a real data example for
finding mutations associated with drug resistance in HIV-1 patients.

本文提出了一种模型无关的 knockoff 生成器，该生成器通过潜在变量表示近似特征之间的相关性结构，进而达到了对 FDR 的控制和更好的功效，可以在 HIV-1 患者中找到与药物抗性相关的突变。