School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Methods Mol Biol. 2022;2404:43-52. doi: 10.1007/978-1-0716-1851-6_2.
The new flow of high-throughput RNA secondary structure data coming from different techniques allowed the further development of machine learning approaches. We developed CROSS and CROSSalive, two algorithms trained on experimental data able to predict the RNA secondary structure propensity both in vitro and in vivo. Since the in vivo folding of RNA molecules depends on multiple factors due to the cellular crowded environment, prediction is a complex problem that needs additional calculations for the interaction with proteins and other molecules. In the following chapter, we will describe the differences in predicting RNA secondary structure propensity using experimental data as input for an Artificial Neural Network (ANN) in vitro and in vivo.
来自不同技术的高通量 RNA 二级结构数据的新流动使得机器学习方法得到了进一步的发展。我们开发了 CROSS 和 CROSSalive 这两种算法，它们是基于实验数据进行训练的，可以预测体外和体内的 RNA 二级结构倾向。由于 RNA 分子在细胞拥挤的环境中受到多种因素的影响，因此体内折叠是一个复杂的问题，需要与蛋白质和其他分子的相互作用进行额外的计算。在下一章中，我们将描述使用实验数据作为输入的人工神经网络 (ANN) 在体外和体内预测 RNA 二级结构倾向的差异。
