基于深度学习神经网络的急性髓系白血病FLT3-ITD突变蛋白质组学分析
Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network
摘要：
深度学习可以显著有益于癌症蛋白质组学和基因组学。在这项研究中，我们试图确定一组与新诊断的急性髓性白血病患者FLT 3-ITD突变相关的关键蛋白质。一种深度学习网络，由形成分层模型的自动编码器组成，从该模型中提取高级特征，而无需标记训练数据。降维将关键蛋白质的数量从231减少到20。深度学习发现FLT 3-ITD突变与这20种关键蛋白质的水平之间存在良好的相关性（准确性97%，灵敏度90%，特异性100%）。我们的深度学习网络可以研究20种与FLT 3-ITD相关性最强的蛋白质。这项研究的结果允许一种新的方法来确定FLT 3-ITD突变中的关键蛋白质通路，并为癌症蛋白质组学和基因组学中大数据建模的准确方法提供概念验证。
英文摘要：
Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempt to determine a set of critical proteins that are associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Learning network consisting of autoencoders forming a hierarchical model from which high-level features are extracted without labeled training data. Dimensional reduction reduced the number of critical proteins from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD mutation with the levels of these 20 critical proteins (accuracy 97%, sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on 20 proteins with the strongest association with FLT3-ITD. The results of this study allow a novel approach to determine critical protein pathways in the FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to model big data in cancer proteomics and genomics.
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