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Prediction of lncRNA-disease Associations Based on Robust Multi-label Learning

[ Vol. 16 , Issue. 9 ]

Author(s):

Jiaxin Zhang, Quanmeng Sun and Cheng Liang*   Pages 1179 - 1189 ( 11 )

Abstract:


Background: Long non-coding RNAs (lncRNAs) are nonprotein-coding transcripts of more than 200 nucleotides in length. In recent years, studies have shown that long non-coding RNAs (lncRNA) play a vital role in various biological processes, complex disease diagnosis, prognosis, and treatment.

Objective: Analysis of known lncRNA-disease associations and prediction of potential lncRNA-disease associations are necessary to provide the most probable candidates for subsequent experimental validation.

Methods: In this paper, we present a novel robust computational framework for lncRNA-disease association prediction by combining the ℓ1-norm graph with multi-label learning. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases using known associations. Then, both lncRNA and disease similarity matrices are adaptively re-weighted to enhance the robustness via the ℓ1- norm graph. Lastly, the association matrix is updated with a graph-based multi-label learning framework to uncover the underlying consistency between the lncRNA space and the disease space.

Results: We compared the proposed method with the four latest methods on five widely used data sets. The experimental results show that our method can achieve comparable performance in both five-fold cross-validation and leave-one-disease-out cross-validation prediction tasks. The case study of prostate cancer further confirms the practicability of our approach in identifying lncRNAs as potential prognostic biomarkers.

Conclusion: Our method can serve as a useful tool for the prediction of novel lncRNA-disease associations.

Keywords:

lncRNA-disease association, similarity matrices, ℓ1-norm graph, multi-label learning, prostate cancer, protein.

Affiliation:

School of Information Science and Engineering, Shandong Normal University, Jinan 250358, School of Information Science and Engineering, Shandong Normal University, Jinan 250358, School of Information Science and Engineering, Shandong Normal University, Jinan 250358

Graphical Abstract:



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