Exploring Neural Network Models for LncRNA Sequence Identification
Exploring Neural Network Models for LncRNA Sequence Identification
复制标题
DOI：
10.1109/bibm49941.2020.9313445
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
J. Miller;D. Adjeroh
中科院分区：
文献类型：
--
作者：
J. Miller;D. Adjeroh
区分长链非编码RNA和蛋白质编码RNA对于分子和细胞生物学非常重要。这个问题可以通过机器学习来解决，特别是人工神经网络。我们探讨了各种网络设计选择对人类LncRNA识别准确性的影响。基于感知器的神经网络模型几乎与更复杂的递归神经网络一样准确，数据的K-mer表示似乎对两者都有帮助。训练数据的大小选择影响结果。这些探索有助于RNA分析的神经网络设计。
Distinguishing long non-coding RNA from protein-coding RNA is important to molecular and cellular biology. The problem can be addressed with machine learning in general and with artificial neural networks in particular. We explore the effects of various network design choices on the accuracy of human LncRNA identification. Perceptron-based neural network models were found to be almost as accurate as more complex recurrent neural networks, and K-mer representations of the data seemed to assist both. Size selection of training data affected results. These explorations could assist in neural network design for RNA analysis.