论文标题
通过DNA和RNA的多种畸变整合了亚型特异性乳腺癌驱动器发现的动态映射基因空间发现
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery
论文作者
论文摘要
驾驶员事件发现是对乳腺癌诊断和治疗的关键需求。特别是，发现驱动因素的亚型特异性可以促使个性化的生物标志物发现和对癌症患者的精确治疗。尽管如此，大多数现有的计算驱动器发现研究主要利用DNA畸变和基因相互作用来利用信息。值得注意的是，癌症驱动器事件不仅是由于DNA畸变，而且还因为RNA的交替而发生，而且从DNA和RNA中整合多型畸变仍然是乳腺癌驱动因素的一项艰巨任务。一方面，不同异差类型的数据格式也彼此不同，称为数据格式不兼容。另一方面，不同类型的畸变表现出在样品之间的不同模式，称为异质性异质性。为了促进对亚型特异性乳腺癌驱动因素的综合分析，我们设计了一个“剪接和绑定”框架，以解决数据格式不兼容和异差异质性的问题。为了克服数据格式不兼容，“剪接步骤”采用了知识图结构，将来自DNA和RNA数据的多类畸变连接到统一的形成中。为了应对像差类型异质性，“融合步骤”采用动态映射基因集成方法来代表通过矢量化概况表示多类型信息。实验还证明了我们方法在DNA和RNA的多类畸变以及亚型特异性乳腺癌驱动因素的整合中的优势。总而言之，我们具有知识图连接和动态映射基因空间融合的“拼接和融合”框架对DNA和RNA的多类畸变数据的动态映射基因融合可以成功地发现具有亚型特异性指示的潜在乳腺癌驱动器。
Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. Especially, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. One the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a "splicing-and-fusing" framework to address the issues of data format incompatibility and aberration type heterogeneity respectively. To overcome the data format incompatibility, the "splicing-step" employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the "fusing-step" adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our "splicing-and-fusing" framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication.
