Abstract:
DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/.
摘要:
DNA测序已经允许发现相当多疾病的遗传原因,为新的疾病诊断铺平了道路。然而,由于缺乏临床样本和记录,罕见疾病的分子原因总是很难确定,显著限制了通过测序技术诊断的罕见孟德尔疾病的数量。因此,临床表型信息成为诊断罕见疾病的主要资源。在这篇文章中,我们采用了表型相似性方法和机器学习方法,建立了4种诊断模型来支持罕见病的诊断。所有诊断模型均使用RAMEDIS的真实医疗记录进行验证。每个模型都提供了前10种候选疾病的列表作为预测结果,结果表明,所有模型的诊断精度都很高(≥98%),最高召回率高达95%,而采用机器学习方法的模型表现出最佳性能。促进罕见病的有效诊断在临床应用,我们开发了基于表型的罕见病辅助诊断系统(RDAD),以辅助临床医生用上述四种诊断模型诊断罕见病.该系统可通过http://www免费访问。unimd.org/RDAD/.