G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.
G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.
复制标题
DOI：
10.1371/journal.pcbi.1010660
复制
发表时间：
2022-10
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
许多基因突变会对承重软组织的结构和功能产生不利影响，临床后遗症往往导致残疾或死亡。遗传学和组织力学表征的平行进展为这些条件提供了重要的见解，但仍然迫切需要整合这些信息。我们提出了一种新的基因型到生物力学表型神经网络（G2Φnet），用于表征和分类软组织的生物力学特性，这些特性可作为组织健康或疾病的重要功能读数。我们说明了我们的方法的实用性，通过推断的非线性，基因型依赖性的组成行为的主动脉的四个小鼠模型涉及的缺陷或不足的细胞外成分。我们表明，G2Φ网可以推断生物力学响应，同时通过利用有限的，嘈杂的，和非结构化的实验数据归因于相关的基因型。更广泛地说，G2Φnet提供了一种强大的方法和范式转变，用于定量关联基因型和生物力学表型，有望更好地了解它们在生物组织中的相互作用。我们介绍了G2Φnet，这是一种新的科学机器学习方法，可以更好地量化临床上重要的组织的宏观生物力学特性，并与潜在的基因突变直接相关。G2Φ网络可以利用有限的、有噪声的和非结构化的实验数据来捕获软组织的基因型依赖的生物力学特性。学习的本构关系对小数据是鲁棒的，并且可推广到看不见的组织。G2Φnet为理解生物组织中基因型和生物力学表型之间的关系提供了一个强有力的工具，在软组织力学、机械生物学和相关临床应用中具有巨大的潜力。
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2Φnet can infer the biomechanical response while simultaneously ascribing the associated genotype by utilizing limited, noisy, and unstructured experimental data. More broadly, G2Φnet provides a powerful method and a paradigm shift for correlating genotype and biomechanical phenotype quantitatively, promising a better understanding of their interplay in biological tissues. We introduce G2Φnet, a novel scientific machine learning approach that enables both a better quantification of macroscale biomechanical properties of tissues that are important clinically and a direct association with an underlying genetic mutation. G2Φnet can capture the genotype-dependent biomechanical properties of soft tissues by utilizing limited, noisy, and unstructured data from experiments. The learned constitutive relation is robust to small data, and generalizable to unseen tissue. G2Φnet provides a powerful tool for understanding relationships between genotype and biomechanical phenotype in biological tissues, promising great potential in soft tissue mechanics, mechanobiology, and related clinical applications.
登录
查看更多内容
DOI：
10.1161/atvbaha.117.309079
发表时间：
2017-05-01
影响因子：
8.7
作者：
Jiao, Yang;Li, Guangxin;Tellides, George
通讯作者：
Tellides, George
影响因子：
3.9
作者：
Bellini, C.;Bersi, M. R.;Humphrey, J. D.
通讯作者：
Humphrey, J. D.
CPINet：基于CNN-LSTM自动去噪的路径依赖本构模型参数识别
影响因子：
4.1
作者：
Guo, Zhenfei;Bai, Ruixiang;Yan, Cheng
通讯作者：
Yan, Cheng
影响因子：
4.1
作者：
Huang, Daniel Z.;Xu, Kailai;Darve, Eric
通讯作者：
Darve, Eric
影响因子：
20.1
作者：
Huang J;Davis EC;Chapman SL;Budatha M;Marmorstein LY;Word RA;Yanagisawa H
通讯作者：
Yanagisawa H