Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
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发表时间：
2018-10
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通讯作者：
Ehsan Hajiramezanali;Siamak Zamani Dadaneh;Alireza Karbalayghareh;Mingyuan Zhou;Xiaoning Qian
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作者：
Ehsan Hajiramezanali;Siamak Zamani Dadaneh;Alireza Karbalayghareh;Mingyuan Zhou;Xiaoning Qian
精准医学旨在通过利用最新的基因组规模高通量分析技术（包括下一代测序（NGS））进行个性化预后和治疗。然而，翻译NGS数据面临着一些挑战。首先，NGS计数数据往往过于分散，需要适当的建模。其次，与涉及的分子数量和系统复杂性相比，用于研究复杂疾病（如癌症）的可用样本数量往往有限，特别是考虑到疾病的异质性。关键问题是我们是否可以整合来自所有不同来源或领域的可用数据，以实现基于NGS计数数据的可重现疾病预后。在本文中，我们开发了一种贝叶斯多域学习（BMDL）模型，该模型基于分层负二项分解推导出过度分散计数数据的域相关潜在表示，即使特定癌症类型的样本数量很小，也可以进行准确的癌症亚型分型。来自癌症基因组图谱（TCGA）的模拟数据集和NGS数据集的实验结果表明，BMDL在有效的多领域学习中具有很大的潜力，而不会产生现有多任务学习和迁移学习方法中常见的“负迁移”效应。
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity. The key question is whether we may integrate available data from all different sources or domains to achieve reproducible disease prognosis based on NGS count data. In this paper, we develop a Bayesian Multi-Domain Learning (BMDL) model that derives domain-dependent latent representations of overdispersed count data based on hierarchical negative binomial factorization for accurate cancer subtyping even if the number of samples for a specific cancer type is small. Experimental results from both our simulated and NGS datasets from The Cancer Genome Atlas (TCGA) demonstrate the promising potential of BMDL for effective multi-domain learning without ``negative transfer'' effects often seen in existing multi-task learning and transfer learning methods.