The Wide and Deep Flexible Neural Tree and Its Ensemble in Predicting Long Non-coding RNA Subcellular Localization

摘要

The long non-coding RNA (lncRNA) is a hot research topic among researchers in the field of biology. Recent studies have illustrated that the subcellular localizations carry salient information to understand the complex biological functions. However, the experimental setup cost and the computational cost to identify the subcellular localization of lncRNA is too high. Therefore, there is a need of some efficient and effective methods to predict the lncRNA subcellular locations. In this paper, a wide and deep flexible neural tree (FNT) is proposed to predict the subcellular localization of lncRNA. The wide component has ability to memorize the original input features, while the deep component has ability to automatically extract hidden features. To fully exploit lncRNA sequence information, we have extracted seven features which are further fed to four wide and deep FNT classifiers respectively. By ensemble four classifiers, it can predict 5 subcellular localizations of lncRNA, including cytoplasm, nucleus, cytosol, ribosome and exosome.