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