面向全基因组关联研究的正样本无标签学习表型分析
Phenotyping with Positive Unlabelled Learning for Genome-Wide Association Studies
AI总结：
针对电子健康记录噪声导致的表型错误分类会削弱GWAS关联检测能力的问题，提出结合锚定学习与Transformer的AnchorBERT模型，可在更少样本下检测到更多显著基因组关联。
摘要：
表型识别在医疗健康与生命科学领域的实际应用中，对深化我们对疾病生物学的理解发挥着重要作用。电子健康记录（EHR）中存在的复杂性与噪声带来了挑战，推动了机器学习在表型发现中的应用。现有研究多聚焦于挖掘可用于临床决策支持的预测性亚型，而本研究则关注导致表型错误分类的噪声——这类噪声会削弱表型在全基因组关联研究（GWAS）中检测关联的能力。研究提出将锚定学习与Transformer架构相结合的模型AnchorBERT，实验表明该模型能够检测到此前仅在病例数为其5倍的大型联盟研究中才发现的基因组关联。当可用对照样本数量减少50%时，与标准表型定义方法相比，该模型能从GWAS目录中多保留40%的显著基因组关联。
英文摘要：
Identifying phenotypes plays an important role in furthering our understanding of disease biology through practical applications within healthcare and the life sciences. The challenge of dealing with the complexities and noise within electronic health records (EHRs) has motivated applications of machine learning in phenotypic discovery. While recent research has focused on finding predictive subtypes for clinical decision support, here we instead focus on the noise that results in phenotypic misclassification, which can reduce a phenotypes ability to detect associations in genome-wide association studies (GWAS). We show that by combining anchor learning and transformer architectures into our proposed model, AnchorBERT, we are able to detect genomic associations only previously found in large consortium studies with 5$\times$ more cases. When reducing the number of controls available by 50\%, we find our model is able to maintain 40\% more significant genomic associations from the GWAS catalog compared to standard phenotype definitions. \keywords{Phenotyping \and Machine Learning \and Semi-Supervised \and Genetic Association Studies \and Biological Discovery}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
