Predicting lncRNA–disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation

Abstract

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA–disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA–disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA–disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction

“…We conducted a comparative analysis of several LDA prediction methods, including MAGCNSE ( Liang et al, 2022 ), MCHNLDA ( Zhao et al, 2022 ), VGAELDA ( Shi et al, 2021 ), CapsNet-LDA ( Zhang et al, 2022 ), LDAformer ( Zhou et al, 2022 ), and SSMF-BLNP ( Xie et al, 2023 ).…”

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

“…Weights are assigned to neighbors for reassigning values to the target matrix, that is an adjacency matrix consisting of lncRNAs, diseases and miRNA. SSMF-BLNP [ 25 ] is based on the combination of selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP). In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion.…”

LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

“…Xie et al. [ 38–41 ] presented several LDA prediction methods, HAUBRW [ 38 ], LDA-LNSUBRW [ 39 ], RWSF-BLP [ 40 ] and SSMF-BLNP [ 41 ]. HAUBRW [ 38 ] incorporated heat spread, probability diffusion and unbalanced bi-random walk.…”

“…RWSF-BLP [ 40 ] used random walk-based multi-similarity fusion with bidirectional label propagation. SSMF-BLNP [ 41 ] integrated selective similarity matrix fusion and bidirectional linear neighborhood label propagation. In addition, several network-based methods have been developed to identify potential LDAs.…”