MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation

Abstract

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA-miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA-miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA-miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA-miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What's more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA-miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.

Keywords: label propagation; linear reconstruction; multilevel similarity; plant lncRNA-miRNA interaction; theoretical derivation.

FIGURE 1

Dataset processing.

FIGURE 2

MILNP for predictions. Phase 0: Extraction of sequence features and interaction profiles. Phase 1: Calculation of sequence similarity and interaction profile similarity to generate integrated similarity. Phase 2: Label propagation using weighted sum to obtain the final prediction matrix.

FIGURE 3

Impact of parameters on AUC scores of MILNP. (A) Effect of K₁ when fixing K₂, α and β. (B) Effect of K₂ when fixing K₁, α and β. (C–F) Effect of α and β when fixing K₁ and K₂.

FIGURE 4

Performance of MILNP’s top-rank predictions, where the X-axis refers to the top 200 to top 2,000 predictions and the Y-axis refers to the recall generated by MILNP.