Breitenstein Matthew K, Hu Vincent Jy, Bhatnagar Roopal, Ratnagiri Madhavi
Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
School of Medicine, University of California - Irvine, Irvine, CA.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:435-442. eCollection 2019.
Systemic lupus erythematosus is a rare, autoimmune disorder known to affect most organ sites. Complicating clinical management is a poorly differentiated, heterogenous SLE disease state. While some small molecule drugs and biologics are available for treatment, additional therapeutic options are needed. Parsing complex biological signatures using powerful, yet human interpretable approaches is critical to advancing our understanding of SLE etiology and identifying therapeutic repositioning opportunities. To approach this goal, we developed a semi-supervised deep neural network pipeline for gene expression profiling of SLE patients and subsequent characterization of individual gene features. Our pipeline performed exemplar multinomial classification of SLE patients in independent balanced validation (F=0.956) and unbalanced, under-powered testing (F=0.944) cohorts. A stacked autoencoder disambiguated individual feature representativeness by regenerating an input-like(A ') feature matrix. A to A' comparisons suggest the top associated features to be key features in gene expression profiling using neural nets.
系统性红斑狼疮是一种罕见的自身免疫性疾病，已知会影响大多数器官部位。复杂的临床管理问题在于系统性红斑狼疮疾病状态的区分性差且具有异质性。虽然有一些小分子药物和生物制剂可用于治疗，但仍需要更多的治疗选择。使用强大且易于人类解释的方法解析复杂的生物学特征对于加深我们对系统性红斑狼疮病因的理解以及识别治疗重新定位机会至关重要。为了实现这一目标，我们开发了一种半监督深度神经网络管道，用于系统性红斑狼疮患者的基因表达谱分析以及随后对单个基因特征的表征。我们的管道在独立平衡验证（F = 0.956）和不平衡、样本量不足的测试（F = 0.944）队列中对系统性红斑狼疮患者进行了示例性多项分类。堆叠自动编码器通过再生类似输入的（A'）特征矩阵来消除单个特征的代表性歧义。A与A'的比较表明，顶级相关特征是使用神经网络进行基因表达谱分析的关键特征。
