Trans-species learning of cellular signaling systems with bimodal deep belief networks
Trans-species learning of cellular signaling systems with bimodal deep belief networks
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DOI：
10.1093/bioinformatics/btv315
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发表时间：
2015-09-15
期刊：
影响因子：
5.8
通讯作者：
Lu, Xinghua
中科院分区：
文献类型：
--
作者：
Chen, Lujia;Cai, Chunhui;Lu, Xinghua
动机：模式生物在人类疾病的生物医学研究和药物开发中起着关键作用。当务之急是将从模式生物获得的信息/知识翻译给人类。在这项研究中，我们解决了一个跨物种学习的问题：预测人类细胞对不同刺激的反应，基于大鼠细胞对相同刺激的反应。结果：我们假设大鼠和人类细胞有着共同的信号编码机制，但使用不同的蛋白质来传递信号，我们开发了双峰深度信念网络和半限制双峰深度信念网络来表示共同的编码机制并执行跨物种学习。这些“深度学习”模型包括分层组织的潜在变量，能够以分布式方式捕获观察到的蛋白质组数据中的统计结构。结果表明，该模型显着优于两个当前国家的最先进的分类算法。我们的研究证明了使用深层层次模型来模拟细胞信号系统的潜力。
Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli.Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems.