- Open Access
Measuring disease similarity and predicting disease-related ncRNAs by a novel method
- Yang Hu†1,
- Meng Zhou†2,
- Hongbo Shi2,
- Hong Ju3,
- Qinghua Jiang1Email author and
- Liang Cheng2Email author
© The Author(s). 2017
Published: 28 December 2017
Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.
Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.
The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ2 = 0.1315, p = 2.2e-16). Finally, the disease similarity computed by infDisSim was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC (0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting novel disease-related lncRNAs and miRNAs, respectively.
The high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related lncRNAs and miRNAs.
One way to indicate the associations between pair-wise diseases in quantitatively is their similarity. In comparison with the associations, disease similarity can indicate the relationships between diseases of multiple categories more clearly and easily, for instance, cancers . In the previous studies, disease similarity was exploited to compute similarities between protein-coding RNA genes (PCGs), which can help to disclose the complex pathogenesis of diseases . Moreover, disease similarity was also employed to calculate similarities between microRNA genes (miRNAs) [2, 3], and long non-coding RNA genes (lncRNAs) [4–8], respectively, which could be applied for constructing functional network of non-coding RNA genes (ncRNAs). Recently, similarity between diseases was even utilized to predict potential therapeutic drugs for diseases [9–12].
Semantic associations and disease gene associations are often considered to be quantitative for evaluating disease similarity. Semantic associations between diseases were documented in the ontology around disease terms. The most widely used ontology for calculating disease similarity is Disease Ontology (DO) , which is the first ontology to be established around disease terms. DO defines a type of semantic association named ‘IS_A’ relationship, which reflects set inclusion relationships between disease terms . Disease terms of DO could build a directed acyclic graph (DAG) based on the ‘IS_A’ relationship. Disease-related genes were distributed in different sources, such as Comparative Toxicogenomics Database (CTD) , Online Mendelian Inheritance in Man (OMIM) , Gene Reference into Functions (GeneRIFs) , Genetic Association Database (GAD) , and so on.
Three widely used methods for computing the similarity of terms of ontology were presented by Resnik , Lin , and Wang et al.  repectively. All of these three methods were utilized for computing disease similarity by DOSim . Resnik presented Information content (IC) of terms of ontology , and in this method, IC of the most informative common ancestor (MICA) of pair-wise diseases was served as the similarity of them. Due to the IC of the pair-wise terms and the IC of the MICA could contribute to the similarity of them, Lin  improved Resnik’s method. By the contrast of Resnik’s and Lin’s method, Wang et al.  computed the similarity between terms fully based on semantic associations of terms in ontology.
In recent years, three methods for calculating similarity of terms of DO were presented. Disease-related genes have been the focus of all these methods. In another word, the similarity of two diseases was converted to the similarity of the two gene sets of diseases. Mathur and Dinakarpandian first presented to utilize the figure of overlapping genes to calculate disease similarity . Even though two gene sets have no shared genes, these two sets could also be connected by their presence during the same or similar biological process. Therefore, Mathur and Dinakarpandian designed a process-similarity based (PSB) method to compute disease similarity based on biological process terms of Gene Ontology [23, 24]. Besides biological process, co-expression  and protein-protein interaction  could also be employed to similarity of disease-related gene sets [27, 28]. Hence, Cheng et al. combined semantic association and the comprehensive gene functional network to compute disease similarity (SemFunSim) , which performs very well.
Improved knowledge has suggested that semantic associations and disease gene associations are two types of significant associations, which were widely exploited to measure disease similarity. Recent studies focused on incorporating disease gene associations from different views. Eventually, comprehensive gene functional network (GFN) was incorporated in SemFunSim method , in which functional interactions of pair-wise genes were considered. Obviously, it is straightforward to consider that whether the entire network could be completely utilized to measure disease similarity. For this purpose, we designed a novel method, called InfDisSim, to figure out disease similarity by modeling the information flow in the comprehensive GFN in this study.
Web site (Date of download)
http://disease-ontology.org/ (Jun 2016)
http://ctdbase.org/ (Jun 2016)
https://geneticassociationdb.nih.gov/ (Jun 2016)
http://www.omim.org/ (Jun 2016)
http://www.cuilab.cn/lncrnadisease (Jun 2016)
Disease gene association network
Disease-related genes are derived from the latest version of diversed open source sources involving CTD , GAD , GeneRIFs , and OMIM . Disease terms in these databases were distributed to DO according to SIDD . After integrating all of these four widely used sources, 130,144 associations between 3178 disease terms and 11,717 genes were obtained as disease gene association network (Additional file 1).
Comprehensive gene functional network
Comprehensive GFN was estimated from HumanNet , which is built around Homo sapiens. Multiple interactions spanning human mRNA co-expression, protein-protein interaction, protein complex, and comparative genomics data sets, combining with alike lines of evidence from orthologs in yeast, fly and worm are comprehensively analyzed for the network utilizing a probabilistic method. Currently, it contains 476,399 interactions among 16,243 genes .
Disease-related drugs were derived from robust, publicly accessible databases CTD , which elucidates the process that chemicals affect human health. Disease terms in CTD were distributed to DO according to SIDD . As a result, 16,639 associations between 1093 diseases and 3887 drugs were obtained.
Human lncRNA-disease associations [31–36] were incorporated into the lncRNA similarity network (LSN), which was constructed based on disease similarity, to predict potential relationships between diseases and lncRNAs. These associations were derived from a manually curated database LncRNADisease , which provided experimentally supported disease-lncRNA associations. After removing disease terminologies not in DO and deploying of duplicate associations, 602 associations between 167 diseases and 338 lncRNAs were obtained (Additional file 2).
Disease-related human miRNAs were extracted from the Human microRNA Disease Database (HMDD) v2.0 . After manually mapping disease terms of HMDD to DO, we got 5710 associations between 556 miRNAs and 265 diseases (Additional file 3).
Method for calculating disease similarity
In this study, we designed a novel method to compute disease similarity by modelling the information flow in the comprehensive GFN. In the previous study, a tool called ITM Probe  was created for analyzing information flow in the network based on random walk with damping. Currently, three models involving absorbing, emitting, and channel were employed in ITM Probe. According to these three models , the initial nodes which are the starting points of the random walk and the sink nodes which are the ending points of the random walk are regarded as boundary nodes, and the rest of the nodes in the network are regarded as transient nodes. Channel model  was designed for directed information flow, which extends absorbing model that specify the source of the information flow and emitting model that distributes end of information flow.
where G 1, G 2 indicates gene set of t 1 and t 2 , respectively. G MICA is the gene set of t 3 , which is the most informative common ancestor of t 1 and t 2 . And ∣.∣ represents the number of terms in the specified set.
where G root represents gene sets of the root node of the DAG of DO. According to the eq. 5, the semantic similarity between t 1 and t 2 is proportional to ∣G 1∣ and ∣G 2∣, and is inversely proportional to ∣G MICA ∣. Therefore, the proportional relation of Eq. 3 is consistent with the proportional relation of Lin’s method.
Method for predicting disease-related lncRNAs and miRNAs
where P 0 represents the initial probability vector, which changes with the step t and the probability γ, P t is a vector in which the ith element represents the probability of finding the walker at node i and step t, A indicates the column-normalized adjacency matrix of the network. The algorithm was implemented until the difference between P t and P t + 1 falling below 10−10, which indicates all the nodes’ status become stable.
Based on our method, researchers can predict novel lncRNA-disease and miRNA-disease associations based on RWR. Firstly, a LSN (MSN) could be constructed for RWR. A lncRNA (miRNA) has associations with a set of diseases. Hence, similarity between two lncRNAs (miRNAs) could be computed based on their related disease sets, which promotes to construct a LSN (MSN). Then, lncRNAs (miRNAs) could be scored for each disease based on RWR, in which the known lncRNAs (miRNAs) of a disease are considered as seed nodes. For each disease, the unknown lncRNAs (miRNAs) of it could be scored. After ranking the lncRNAs (miRNAs) based on the scores, disease-related lncRNAs (miRNAs) are finally predicted.
Method for validating the performance of InfDisSim
Performance evaluation based on benchmark set
Resnik’s, Lin’s, and Wang’s methods concentrated on sematic associations. Few of disease gene associations were employed by these three methods. With more and more disease gene associations and gene interactions identified, it is easier to study similarity between diseases in molecular level. Fortunately, three methods including PSB, SemFunSim, and InfDisSim have intergrated these associations into semantic associations. It is easy to find the interactions between genes including mRNA co-expression, protein-protein interaction, protein complex, and so on. Although PSB method only applied co-occurrenced biological process of genes, its performance has already been improved. To enhance the performance, SemFunSim and InfDisSim methods employed comprehensive gene functional associations from two different views. And both of these two methods perform excellently.
Figure 4b shows the AUCs of the 100 iterators, which are consistent with the Fig. 4a. From this figure, the average AUCs of the 100 iterators are 0.6223, 0.6538, 0.6851, 0.8824, 0.9832, and 0.9788, respectively.
Relationship between disease similarity by InfDisSim and co-occurrence drugs
Application of disease similarity to the prediction of disease-related lncRNAs
For the sake of showing the usefulness of disease similarity computed by our InfDisSim, we firstly constructed a lncRNA similarity network (LSN) based on disease similarity, and then identified disease-related lncRNAs based on LSN. The similarity of each pair of 111 lncRNAs was computed using the eq. 6. After that, the z-score of each pair of lncRNAs was computed based on these scores. Then, each similarity score gained a one-sided P-value. Finally, all of these lncRNA similarity scores were appiled to construct LSN (Additional file 5).
Application of disease similarity to the prediction of disease-related miRNAs
We also utilized the disease similarity to construct a MSN and predict disease-related miRNAs based on the network. Here, we calculated similarity of each pair of 265 miRNAs and corresponding one-sided P-value. All of these miRNA similarity scores were employed to construct MSN (Additional file 6) for predicting disease-related miRNAs. The performance of the MSN was assessed by leave-one-out cross validation. As a result, we got an AUC of 0.9007.
To identify the disease-related ncRNAs, including lncRNAs and miRNAs, we presented a novel method based on disease similarity using a random walk. With the high AUC performance of predicting disease-related miRNAs and lncRNAs (0.9893, 0.9007), the proposed methods in this paper may also be applied to predict other disease-related modules, e.g. SNP and risk pathways [43, 44].
In this study, we presented a novel method, InfDisSim, to figure out disease similarity by semantic association and disease-related genes. In time of computing similarity based on genes, information flow was modelled into a comprehensive GFN, which is constructed by integrating multiple interactions involving mRNA co-expression, protein-protein interaction, protein complex, and so on. In the precious study, SemFunSim has introduced the interactions of pair-wise genes between different gene set. Here, the whole network was fully employed based on information flow. It introduced a novel view to compute disease similarity.
The performance of InfDisSim was validated employing the benchmark set. The high AUC (0.9786) indicates its excellent performance. Then, we assessed the observation that similar diseases could have common therapeutic drugs. Finally, InfDisSim disease similarity was significant positively correlated with the co-occurrence drugs (Pearson correlation γ2 = 0.1315, p = 2.2e-16; Fig. 5). Therefore, InfDisSim disease similarity could be utilized to predict potential associations between diseases and drugs.
lncRNA similarity and miRNA similarity could be computed based on InfDisSim disease similarity. Here, for all the pairs of lncRNAs (miRNAs), which was applied to construct a LSN (MSN), we calculated their similarities. The network was further used to predicate disease-related lncRNAs (miRNAs). As a result, the high AUC (0.9893, 0.9007) illustrates that the LSN (MSN) is very appropriate for predicting potential associations between diseases and lncRNAs (miRNAs) based on RWR.
The authors thank for editors and reviewers for the improvement of this manuscript.
This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT NSRIF 201856), the National Science and Technology Major Project [Nos: 2016YFC1202302], the National Natural Science Foundation of China (No: 61502125 and 61571152), the National High-tech R&D Program of China (863 Program) [Nos: 2014AA021505, 2015AA020101, 2015AA020108], Heilongjiang Postdoctoral Fund (Grant No. LBH-Z6064, LBH-Z15179), and China Postdoctoral Science Foundation (Grant No. 2016M590291). Publication costs were funded by the Fundamental Research Funds for the Central Universities (Grant No. HIT NSRIF 201856), the National Natural Science Foundation of China (No: 61502125 and 61571152), Heilongjiang Postdoctoral Fund (Grant No. LBH-Z6064, LBH-Z15179), and China Postdoctoral Science Foundation (Grant No. 2016M590291).
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
About this supplement
This article has been published as part of BMC Medical Genomics Volume 10 Supplement 5, 2017: Selected articles from the IEEE BIBM International Conference on Bioinformatics & Biomedicine (BIBM) 2016: medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-5.
LC, and YH conceived and designed the experiments. LC, MZ, HS, HJ, QJ analysed data. LC wrote this manuscript. All authors read and approved the final manuscript.
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