标题： 基于贝叶斯多领域学习的癌症亚型发现方法从下一代测序计数数据中 显示英文标题

标题： Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

摘要： 精准医学旨在通过利用最近的基因组规模高通量分析技术，包括下一代测序（NGS），实现个性化的预后和治疗。 然而，将NGS数据进行转换面临几个挑战。 首先，NGS计数数据通常过度分散，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，可用于研究复杂疾病（如癌症）的样本数量通常有限，尤其是在考虑疾病异质性的情况下。 关键问题是，我们是否可以整合所有不同来源或领域中的可用数据，以基于NGS计数数据实现可重复的疾病预后。 在本文中，我们开发了一个贝叶斯多领域学习（BMDL）模型，该模型基于分层负二项式因子分解，从过度分散的计数数据中推导出领域相关的潜在表示，即使某种特定癌症类型的样本数量较少，也能准确进行癌症亚型分类。 来自我们模拟数据和《癌症基因组图谱》（TCGA）的NGS数据集的实验结果表明，BMDL在无需“负面迁移”效应的情况下，具有在有效多领域学习中表现出有前途的潜力，而这种“负面迁移”效应在现有的多任务学习和迁移学习方法中经常出现。 显示英文摘要

摘要： 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.