Indiana University South Bend, South Bend, IN, USA.
Methods Mol Biol. 2021;2190:185-194. doi: 10.1007/978-1-0716-0826-5_8.
Biology has become a data driven science largely due to the technological advances that have generated large volumes of data. To extract meaningful information from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The "black box" nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.
生物学在很大程度上已经成为一门数据驱动的科学，这主要归功于技术的进步，这些进步产生了大量的数据。为了从这些数据集提取有意义的信息，需要使用复杂的建模方法。为此，基于人工神经网络 (ANN) 的建模正日益发挥非常重要的作用。ANN 的“黑箱”性质成为提供模型生物学解释的障碍。本文描述了使用卡尺随机化方法为生物系统构建模型并对其进行解释的基本步骤，以捕获复杂信息。
