From Genotype to Phenotype: Augmenting Deep Learning with Networks and Systems Biology.
From Genotype to Phenotype: Augmenting Deep Learning with Networks and Systems Biology.
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DOI：
10.1016/j.coisb.2019.04.001
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发表时间：
2019-06-01
影响因子：
3.7
通讯作者：
Lewis, Nathan E
中科院分区：
文献类型：
--
作者：
Gazestani, Vahid H;Lewis, Nathan E
细胞作为一个复杂的系统，由多种相互作用的生物分子组成，这些生物分子以动态的层次模块排列。深度学习方法的最新进展现在允许人们在学习过程的架构中编码这种丰富的现有知识，从而为模型提供训练数据中不存在的知识。通过在架构中编码生物网络，可以开发灵活的深度模型，通过分子网络传播信息，以成功地对细胞状态进行分类。此外，架构中的这种灵活性可以被用来对真实的生物系统的分层结构进行建模，有效地将基因水平的数据转换为对细胞表型具有最终影响的通路水平的信息。此外，这样的模型可能需要更少的训练样本，在不同的生物背景下更具普遍性，并且可以做出与当前对生物系统内部工作的理解更一致的预测。
Cells, as complex systems, consist of diverse interacting biomolecules arranged in dynamic hierarchical modules. Recent advances in deep learning methods now allow one to encode this rich existing knowledge in the architecture of the learning procedure, thus providing the models with the knowledge that is absent in the training data. By encoding biological networks in the architecture, one can develop flexible deep models that propagate information through the molecular networks to successfully classify cell states. Moreover, this flexibility in the architecture can be harnessed to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an ultimate impact on cell phenotype. Furthermore, such models could require fewer training samples, are more generalizable across diverse biological contexts, and can make predictions that are more consistent with the current understanding on the inner-working of biological systems.