LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion

Abstract

Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide significant information on its function. Due to the lack of experimental data, the number of lncRNAs is very limited, experimentally verified subcellular localization, and the numbers of lncRNAs located in different organelle are wildly imbalanced. The prediction of subcellular location of lncRNAs is actually a multi-classification small sample imbalance problem. The imbalance of data results in the poor recognition effect of machine learning models on small data subsets, which is a puzzling and challenging problem in the existing research. In this study, we integrate multi-source features to construct a sequence-based computational tool, lncLocation, to predict the subcellular location of lncRNAs. Autoencoder is used to enhance part of the features, and the binomial distribution-based filtering method and recursive feature elimination (RFE) are used to filter some of the features. It improves the representation ability of data and reduces the problem of unbalanced multi-classification data. By comprehensive experiments on different feature combinations and machine learning models, we select the optimal features and classifier model scheme to construct a subcellular location prediction tool, lncLocation. LncLocation can obtain an 87.78% accuracy using 5-fold cross validation on the benchmark data, which is higher than the state-of-the-art tools, and the classification performance, especially for small class sets, is improved significantly.

Keywords: logarithm-distance of Hexamer; multi-source features; subcellullar location; the binomial distribution-based filtering.

Figure 1

New fea.Tuple training results on each model.

Figure 2

New fea.Bio training results on each model.

Figure 3

Connecting new fea.Tuple and new fea.Bio training results on each model.

Figure 4

Input and output part of the screenshot of the lncLocation web server.

Figure 5

Pie chart of the distribution ratio of lncRNA in four organelles.

Figure 6

The flowchart of lncLocation. (A) Multi-source feature extraction; (B) Feature learning and model selection.