IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method

Abstract

Background: It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models. Results: In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM. Conclusions: Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential diseaseassociated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/ CDMB-lab/IDSSIM.

NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk

“…We chose the Mammalian ncRNA-Disease Repository (MNDR) database ( Cui et al, 2018 ), from which the known lncRNA-disease interactions were downloaded. After data cleaning, 1,680 known interactions between 190 diseases and 89 lncRNAs were selected ( Fan et al, 2020 ). We performed the same experiment as above, and Figure 5 shows the final computational results.…”

“…We downloaded known lncRNA-disease associations from the 2017-version LncRNADisease database ( Chen et al, 2013 ) ( http://www.cuilab.cn/lncrnadisease ). After conducting data quality control and data cleaning, 701 known experimentally validated interactions between 157 diseases and 82 lncRNAs were acquired, as previously reported ( Fan et al, 2020 ). and indicate the numbers of lncRNAs and diseases, respectively.…”

“…Directed acyclic graphs (DAGs) have been widely used to compute the semantic similarity between diseases when predicting potential lncRNA-disease interactions ( Chen et al, 2017 ). Here, the disease semantic similarity was calculated as previously reported ( Fan et al, 2020 ). First, the Medical Subject Headings (MeSH) descriptors of the diseases we needed were downloaded from the National Library of Medicine ( http://www.nlm.nih.gov/ ) ( Wang et al, 2010 ).…”

Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

“…In 2020, Zeng et al ( Zeng et al, 2020 ) proposed SDLDA, an LDAP model based on singular value decomposition and deep learning. Fan et al ( Fan et al, 2020 ) proposed IDSSIM, a calculation model of lncRNA functional similarity based on improved disease semantic similarity. Tan et al ( Tan et al, 2020 ) proposed a multi-view consensus graph learning model to predict lncRNA–disease association.…”

Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

“…precise resolution [43,44]. In addition to genes in coding regions, our model is also potentially applicable for the analysis of attribute filling of noncoding regions in bioinformatics [45]. Furthermore, incorporating the technique of recent advanced artificial intelligence is also a promising direction for improvement [46,47].…”