Young Jonathan D, Ren Shuangxia, Chen Lujia, Lu Xinghua
Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Cancers (Basel). 2023 Jul 29;15(15):3857. doi: 10.3390/cancers15153857.
Cancer is a disease of aberrant cellular signaling resulting from somatic genomic alterations (SGAs). Heterogeneous SGA events in tumors lead to tumor-specific signaling system aberrations. We interpret the cancer signaling system as a causal graphical model, where SGAs affect signaling proteins, propagate their effects through signal transduction, and ultimately change gene expression. To represent such a system, we developed a deep learning model called redundant-input neural network (RINN) with a transparent redundant-input architecture. Our findings demonstrate that by utilizing SGAs as inputs, the RINN can encode their impact on the signaling system and predict gene expression accurately when measured as the area under ROC curves. Moreover, the RINN can discover the shared functional impact (similar embeddings) of SGAs that perturb a common signaling pathway (e.g., PI3K, Nrf2, and TGF). Furthermore, the RINN exhibits the ability to discover known relationships in cellular signaling systems.
癌症是一种由体细胞基因组改变（SGA）导致的异常细胞信号传导疾病。肿瘤中异质性的SGA事件会导致肿瘤特异性信号系统畸变。我们将癌症信号系统解释为一种因果图形模型，其中SGA影响信号蛋白，通过信号转导传播其效应，并最终改变基因表达。为了表示这样一个系统，我们开发了一种名为冗余输入神经网络（RINN）的深度学习模型，其具有透明的冗余输入架构。我们的研究结果表明，通过将SGA用作输入，RINN可以编码它们对信号系统的影响，并在以ROC曲线下面积衡量时准确预测基因表达。此外，RINN可以发现干扰共同信号通路（例如PI3K、Nrf2和TGF）的SGA的共享功能影响（相似嵌入）。此外，RINN还表现出发现细胞信号系统中已知关系的能力。
