 Research
 Open Access
 Published:
PDAPRGCN: identification of Piwiinteracting RNAdisease associations through subgraph projection and residual scalingbased feature augmentation
BMC Bioinformatics volumeÂ 24, ArticleÂ number:Â 18 (2023)
Abstract
Background
Emerging evidences show that Piwiinteracting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNAdisease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a costeffective strategy. However, few computational methods have been developed so far.
Results
Here, we proposed an endtoend model, referred to as PDAPRGCN (PDA prediction using subgraph Projection and Residual scalingbased feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNAdisease associations represented as a graph, we applied subgraph projection to construct piRNApiRNA and diseasedisease subgraphs for the first time, followed by a residual scalingbased feature augmentation algorithm for node initial representation. Then, we adopted graph convolutional network (GCN) to learn and identify potential PDAs as a link prediction task on the constructed heterogeneous graph. Comprehensive experiments, including the performance comparison of individual components in PDAPRGCN, indicated the significant improvement of integrating subgraph projection, node feature augmentation and dualloss mechanism into GCN for PDA prediction. Compared with stateoftheart approaches, PDAPRGCN gave more accurate and robust predictions. Finally, the case studies further corroborated that PDAPRGCN can reliably detect PDAs.
Conclusion
PDAPRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.
Background
Piwiinteracting RNAs (piRNAs), a special kind of small noncoding RNA molecules with 26â€“31 nucleotides, are important regulatory factors in multiple biological processes through interacting with PIWI proteins [1]. Recently, a variety of evidences have confirmed that piRNAs play significant roles in transposon silencing and heterochromatin [2]. Meanwhile, irregular expression or modifications of piRNA are highly associated with complex diseases [3,4,5]. Owing to their critical role as a type of potential biomarker, exploring piRNAdisease associations (PDAs) is not only helpful for revealing the molecular mechanisms of diseases at noncoding RNA level, but also critical for further boosting the diagnosis, treatment, and prevention of human diseases. Conventional biological wet experiments for uncovering PDAs are often afflicted with high cost and timeconsuming. Hence, it would be imperative to construct efficient and accurate models for identifying potential PDAs via computational methods.
Over the past few years, despite several databases involved in piRNAs such as piRBase [6], piRDisease [7], piRPheno [8], and MNDR [9] have been released, experimentally verified PDAs are far from comprehensiveness. To date, only several computational models have been put forward. Among them, iPiDAsHN adopted convolutional neural network (CNN) to extract features and trained Support Vector Machine (SVM) to select negative samples to identify potential PDAs [10]. Afterward, iPiDiPUL extracted key features and conducted dimension reduction by principal component analysis over feature vector based on positive unlabeled learning [11]. GAPDA treated each known piRNAdisease association pair as a node in their reconstructed graph and employed graph attention network to make representation learning [12]. SPRDA applied piRNA/disease similarity network to form a duplex network, then predicted PDAs as a matrix completion problem by structural perturbation algorithm [13].
Despite their successes, these models either generally regard known PDAs as feature data in Euclidean space (e.g., iPiDAsHN and iPiDiPUL), or reconstruct an abstract graph derived from original PDAs to simply transform link prediction into node classification problem (e.g., GAPDA and SPRDA). We argue that PDAs are naturally rich in structural features as a linked graph. Consequently, by implementing the raw PDA data as the intrinsic structure of a PDA graph, more accurate predictions are possible.
Beside GAPDA and SPRDA, with recent indepth advances on graph theory and network science, various biomedical entity association prediction (EAP) approaches based on graph convolutional network (GCN) [14] have been proposed. In view of that, fitting the actual PDAs in GCN may be beneficial. Therefore, it is promising to adopt GCN to model PDA data as heterogeneous graph capable of making precise PDA prediction.
In this paper, we proposed a method, PDAPRGCN, which incorporated three sequential strategies into GCN to detect potential PDAs. Specifically, we first constructed piRNApiRNA and diseasedisease subgraph separately by projecting the PDAs as links in the piRNAdisease graph. Then, to obtain highquality initial representations, residual scalingbased node feature augmentation was designed to initialize the node feature to be propagated and aggregated in GCN layers. Finally, we introduced a dualloss mechanism in an endtoend GCN training process: accurately predicting relations by cross entropyloss; adaptively constraining binary classification error between sensitivity and specificity by sensitivityâ€“specificity loss. To evaluate the performance of PDAPRGCN, extensive in silico experiments were performed. PDAPRGCN achieved AUC of 0.9464, AUPR of 0.9190 on main dataset under fivefold crossvalidation (5CV), outperforming existing stateoftheart methods. Case studies further confirmed the efficacy of PDAPRGCN on PDA prediction.
Results
Experiment design
To evaluate the overall performance and validate individual components of our model, comprehensive experiments were designed. First, 5CV was conducted. To explore the effect of link embedding, clustering of positive and negative links was visually checked. Then, prediction performances were compared among PDAPRGCN and stateoftheart methods. Subsequently, we presented the following comparisons: projection subgraph verses similarity subgraph; data augmentation similarity subgraph verses original similarity subgraph; residual scalingbased node feature augmentation verses original feature; balanced samples verse unbalanced samples in term of positivetonegative ratio. The comparisons were made to evaluate the components of PDAPRGCN and each of their influences on PDA identification.
Prediction performance of PDAPRGCN was evaluated using the area under the receiver operating characteristic curve (AUC) and the area under precisionâ€“recall curve (AUPR). Relevant evaluation metrics include accuracy, precision, recall and F1 (their definitions can be found in Additional file 1: Note S1). It is important to note that AUPR is more suitable than AUC on evaluating model performance with unbalanced samples which are overwhelmed by negative samples, since it punishes false positives more stringently.
For PDAPRGCN, the parameter epoch was set to 12,000 after optimization. The learning rate was set to 0.002 and the dropout rate was set to 0.2.
Performance of PDAPRGCN and comparison with stateoftheart methods
We conducted 5CV to compare model performance. Following the setup of previous methods, we took known PDAs as positive samples and randomly selected the same number of unlabeled PDAs as negative samples. For each fold, randomly divided subset containing positive and equalsize negative samples were held out as training data and the rest were used as test data.
As shown in Table 1 and Fig. 1, PDAPRGCN obtains mean AUC of 0.9464, as well as mean AUPR of 0.9190 on the main dataset, which validate the strategies of PDAPRGCN on detecting potential PDAs. It is worth noting that although the AUPR in each fold is slightly lower than corresponding AUC, the average AUPRs keep at similar level with AUCs. This may be attributed to the weight put on the sensitivityâ€“specificity loss function in the dualloss optimization.
Furthermore, to comprehend learning abilities of PDAPRGCN, we mapped the link embedding derived from node embedding by our model into a 2D space by tSNE [15] and UMAP [16], respectively. As shown in Fig. 2, the predicted PDAs by PDAPRGCN are spatially clustered for positive and negative links. The visualization suggests that piRNApiRNA/diseasedisease subgraph construction via median subgraph projection and residual scalingbased node feature augmentation are successful for both piRNAs and diseases.
Then, we compared our model with existing baseline methods, i.e., iPiDiPUL, iPiDAsHN, GAPDA, SPRDA and LPIdeepGBDT [17]. For practical machine learning applications, it is an effective strategy to select negative samples. iPiDAsHN and iPiDAPUL do well in this point. The main aim of our study is to focus on the model performance and generalization ability on balanced sample structure using random negative samples that did not make any selection. To better assess the performance of PDAPRGCN, we compared the deep learningbased model (i.e., LPIdeepGBDT) in which its feature space was designed in Euclidean space.
As shown in Table 2, our model has optimal prediction performance in six evaluation metrics, when applied on independent piRDisease dataset. In comparison with iPiDAsHN and iPiDAPUL, the performance of PDAPRGCN is completely advantageous, even when unselected its negative samples. For LPIdeepGBDT, despite its success in lncRNAprotein interaction identification, the model regarded molecular association data as feature data in Euclidean space and adopted CNN to extract features. PDAs are naturally rich in structural features as a linked graph. GCN in our proposed PDAPRGCN model gave better predictions as an encoder to model and represent features than the LPIdeepGBDT would do. Besides, although SPRDA was designed in Graph space and shows the next highest AUC, it does not get the samelevel AUPR, which limits the performance to some extent. For the five methods with uneven AUPR, our model shows significant improvements. Together, the consistent prediction performances of PDAPRGCN on the main and piRDisease datasets support the robustness of our model.
Impact of various types of subgraph construction on model performance
Following the previous methods, that is, using similarity to construct subgraph, we explored the influence of two subgraph construction approaches: projection subgraph and similarity subgraph. Considering different node feature types, we first initialized node features for each node (piRNA or disease) respectively, then conducted the comparison to evaluate the impact of similaritybased subgraph construction under 5CV. Besides, because we have already utilized the similarity to construct subgraph, we reset the feature for each node. Here, aside from constant, we can take role2vec as node prerepresentation. Role2vec has proper representation effect and works well in unsupervised graph embedding [18]. Specifically, similar to model ablation study, we designed three similaritybased subgraph construction models below. For convenience, we use OSG to denote original similarity subgraph, NF to denote node feature and rsSG to denote residual scalingbased similarity subgraph.

OSG with constant NF: it uses the original similarity to construct subgraph and set all node feature to 1.

OSG with role2vec NF: it employs the original similarity to construct subgraph, but uses role2vec to learn node primary representation instead of constant.

rsSG with role2vec NF: under the premise of using role2vec for NF, it adopts residual scalingbased similarity data augmentation to construct subgraph.
Figure 3 displays the performance comparisons between PDAPRGCN and the three similaritybased subgraph construction methods in terms of AUC and AUPR. We observed that using original similarity to construct subgraph has poor performance. Particularly, for OSG with constant NF, it has AUC of 0.5 and AUPR of 0.75. For OSG with role2vec NF, considering the impact of node initial feature, after conducting node primary representation by role2vec, the model achieved an AUCâ€‰>â€‰0.9 and an AUPRâ€‰>â€‰0.8. Furthermore, to evaluate the effect of incorporating similarity data augmentation into model, we designed and tested rsSG with role2vec NF. As expected, its AUC gets the level of 0.93 and AUPR increases to 0.90. The improvement validates the strategy of incorporating data augmentation into similarity subgraph compared with original similarity subgraph. Although the setup of similarity subgraph with similarity data augmentation (i.e., rsSG) works well under role2vec, PDAPRGCN remains the best solution.
Impact of projection subgraph and node feature augmentation on model performance
In order to investigate the importance of projection subgraph (AM) and node feature augmentation (NIR), we designed two respective schemes. The first scheme was designed for NIR as following. For subgraph, we fixed the projection subgraph; for node feature, we only adopted original similarity profile with Stacked Autoencoder (i.e., SAE is used to reduce noise). To evaluate the impact of AM, the second scheme was designed as following. For node feature, we fixed node features augmentation; for subgraph, we integrated original similarity profile information into the projection subgraph. Here, the rationale of inputting original similarity profile information into the projection subgraph is to examine the influence of perturbations on projection subgraph in our model.
As shown in Fig. 4, without node feature augmentation, the first scheme performs poorly. By contrast, owing to the incorporation of the residual scalingbased node feature augmentation, the AUC and AUPR of PDAPRGCN increase dramatically. It indicates node feature augmentation is a promising strategy for detecting potential PDAs. For the second scheme, with some perturbations on similarity profile information, the prediction performance decreases sharply. It suggests that the model is sensitive to similarity profiles even if node feature augmentation is not changed, thus substantiating the significant roles of projection subgraph in recognizing possible piRNAdisease links. Together, the experiments showed that for node feature augmentation and projection subgraph, removing either one will seriously hinder the performance of prediction. Thus, node feature augmentation and projection subgraph are complementary to each other in PDAPRGCN. Integrating them both into our model can jointly enhance the PDA prediction.
Impact of unbalanced sample structure on model performance
For practical applications, it is important to evaluate model performance on unbalanced sample structure in terms of positive and negative proportion. We built three sample setups with positivetonegative ratio of 1:1, 1:5, and 1:10, respectively. Then we trained and tested our model on the samples under 5CV. Since AUPR punishes false positives more severely than AUC, it is more instructive on model performance when negative samples are much more than positives. As shown in Table 3, PDAPRGCN has reasonable performance in terms of AUPR in all the samples. As expected, AUPR decreases with the decease of positivetonegative ratio from 1:1, 1:5 to 1:10. Nevertheless, the lowest AUPRs (0.5952 on main dataset, and 0.6263 on piRDisease dataset) keep at a moderate and practically acceptable level. This behavior suggests a weak dependency of model performance on sample structure. In total, our method performs quite well in samples with a wide range of positivetonegative ratios.
Case studies
Case studies on breast neoplasm, renal cell carcinoma, head and neck neoplasms and alzheimer disease were conducted to identify the potential piRNAs associated with each of the four diseases, respectively. For fairness of comparison, we applied PDAPRGCN to independent dataset in which we ensured node information of collected piRNAbreast neoplasm/renal cell carcinoma/head and neck neoplasms/Alzheimer disease data were included in our training dataset (the main dataset) without edge information. The top ten predicted breast neoplasmrelated piRNAs, top ten predicted renal cell carcinomarelated piRNAs, top ten predicted head and neck neoplasmsrelated piRNAs and top five predicted Alzheimer diseaserelated piRNAs were used to assess the applicability of PDAPRGCN. As shown in Table 4, the predicted piRNAs are confirmed by the independent piRDisease database. Together, the case studies further substantiate the superior performance of PDAPRGCN on PDA prediction.
Discussion
Based on the performance evaluation and experiments conducted, the advantages of PDAPRGCN are summarized as follows. First, it introduced a median subgraph projection approach for subgraph construction to capture the most likely links between piRNAs/diseases based on local centrality. This treatment is distinct from the commonly used similarity construction approaches. The outstanding performance suggests the potential of applying the strategy on other EAP problems. Second, a residual scalingbased node feature augmentation was designed and leveraged for a compact and highquality initial node feature representation. Sequencebased kmer similarity profile of piRNA and semantic similarity profile of disease contains redundant information. As a feature augmentation technique, residual scaling can effectively improve the final embedding. Third, a dualloss mechanism was introduced, which can optimize the discrimination of binary samples especially for data containing unbalanced positive and negative samples.
Most methods of heterogeneous graph/networkbased EAP via GCN modeling, by their very nature, mainly focus on how to construct the subgraph and initially represent the node [12, 19, 20]. It should be noted that similarity can be implemented in two alternative ways: construct subgraph as similarity graph; characterize the node feature as similarity profile. The experiment on our model showed the first way performed not well for PDA prediction. Instead, it is better to utilize it as a kind of similarity profile for node feature. This way, the enhanced similarity profile information among entities can be preserved and propagated with the layerwise aggregation via GCN, thus, to ensure proximity between similar node embeddings and separability between dissimilar ones. Working with projection subgraph, the enhanced node features in our model can effectively represent the initially node states for following graph convolution process.
The limited number of known PDAs relative to all piRNAdisease pairs lead to the issue of unbalanced sample composition. Notwithstanding, various work train and test their models on balanced data [21,22,23], thereby limiting their applications in many practical scenarios. Prevailingly, researchers tend to subsample negative samples or equivalently decrease their weights in the optimization process [24]. This way, information in the negative samples might be underrepresented. In contrast, for PDAPRGCN, we increased the proportion of negative samples in the data as typically practical scenarios. The AUC increases with the inclusion of more negative samples, thereby demonstrating the efficacy of incorporating extra negative samples. We also observed that although AUPR decreased significantly at the same time, it is still at a competitive and practically acceptable level. It supports the power of dualloss mechanism on unbalanced binary samples. Together, they moderate AUPRs on severely unbalanced samples suggest PDAPRGCN as a promising solution on this challenging task.
Although this study incorporated subgraph projection to facilitate potential PDA prediction, the method involved projection node screening that relies on degree distribution of original dataset as a relational graph. In the future, we plan to adopt weighted subgraph projection to further improve subgraph construction. With more sophisticated biological information incorporated, the power of subgraph projection can be fully utilized to decipher biological associations more efficiently and accurately.
Conclusions
In this paper, we proposed PDAPRGCN, a computational method for potential PDA identification. Its prediction performance was evaluated by various comparative experiments extensively. Compared with the existing methods, PDAPRGCN shows outstanding performance on PDA prediction. Moreover, competitive AUCs and AUPRs of PDAPRGCN on highly unbalanced samples support its applicability as a screening tool in practice, where positive PDAs only represent a small proportion of all piRNAdisease pairs.
Materials and methods
Datasets and preprocessing
The manualcurated piRNAdisease datasets were collected from publicly available piRBase2.0 and MNDR v3.1 as the main dataset used in our model. Here, we only chose those experimentally verified PDA pairs. The sequence information of each piRNA can be obtained in the two databases. We filtered out those nonhuman PDA pairs from MNDR v3.1. Besides, it is worth mentioning that many nodes (i.e., piRNAs) with degreeâ€‰=â€‰1 exist in the datasets. Considering the influence of degree on graphbased methods, as shown in Additional file 1: Figs. S1 and S2, we filtered out the nodes with degreeâ€‰=â€‰1. For a graph containing N nodes, the degree distribution is defined as follows:
where P_{k} is the degree distribution and N_{k} denotes the number of nodes with degreeâ€‰=â€‰k, and P_{k} should meet \(\sum\limits_{k = 0}^{\infty } {P_{k} } = 1\).
Finally, after performing the inclusion of identifier unification, deredundancy and deletion of the irrelevant items from the two databases (i.e., piRBase2.0 and MNDR v3.1), we got the main dataset including 3446 pairs of known associations among 1478 piRNAs and 24 diseases.
In addition, for independent piRDisease dataset, generally viewed as benchmark dataset by the baseline methods, it consists of 4350 piRNAs, 21 diseases and 4993 known PDAs. We employed it to make comparisons with the five baseline methods.
Medical Subject Headings (MeSH) descriptor data of diseases were downloaded from https://meshb.nlm.nih.gov/.
Method overview
The backbone of a graph is node and edge. How to effectively mine structural information for edges and augment feature information for nodes side by side is a key point for graphbased methods. Based on this, the rationale of our method is to construct appropriate topology associations as adjacency matrix (AM) and enhance node features as node initial representation (NIR) on the heterogeneous graph of PDAs. This way, we can integrate the two components into a GCN and convert the problem of PDA prediction into a graph link prediction task. The flowchart is shown in Fig. 5.
Composition of the PDA heterogeneous graph
Based on graph theory, we can treat the detection of potential PDAs as a link prediction task in graph. Thus, a heterogeneous graph consisting of the piRNApiRNA subgraph, diseasedisease subgraph and known piRNAdisease subgraph is established. Specifically, we can denote them by A^{pp}, A^{dd}, and A^{pd}, then integrate the three subgraphs into a heterogeneous graph G. After aligning nodes of different subgraphs according to the node map, the final adjacency matrix Aâ€‰âˆˆâ€‰R^{(N+M)Ã— (N+M)} of G is defined as follows:
where N is the number of piRNAs, and M is the number of diseases. A^{pp} denotes the projection matrix of piRNApiRNA and A^{dd} denotes the corresponding diseasedisease matrix, while A^{pd} denotes the known PDA matrix and (A^{pd})^{T} denotes its transposition.
Within the full graph, how to effectively construct A^{pp} and A^{dd} subgraph is a key point to infer potential PDAs. Different from those shallow embedding methods such as node2vec [25], LINE [26], and SDNE [27] etc., we more thoroughly considered node feature information combined with PDA heterogeneous graph to jointly learn the node representation in each convolution layer. The detailed construction procedure will be shown in the following sections.
piRNA/disease subgraph construction
In this study, we adopted bipartite graph projection [28] to construct piRNA subgraph and disease subgraph individually. We assume that Pâ€‰=â€‰{P_{1}, P_{2}, â€¦, P_{n}} (nâ€‰=â€‰1478) is the set of piRNA nodes and Dâ€‰=â€‰{D_{1}, D_{2}, â€¦, D_{m}} (mâ€‰=â€‰24) is the set of disease nodes, while PDAsâ€‰=â€‰{P_{a} D_{b}} (1â€‰â‰¤â€‰aâ€‰â‰¤â€‰n and 1â€‰â‰¤â€‰bâ€‰â‰¤â€‰m) is the set of known piRNAdisease association. Given any P_{p} D_{i}â€‰âˆˆâ€‰PDAs and P_{q} D_{i}â€‰âˆˆâ€‰PDAs, (i.e., piRNA p and q are both related to disease i), we can infer the edge P_{p} P_{q} between P_{p} and P_{q} and construct the piRNApiRNA subgraph. Same procedure applies to diseasedisease subgraph construction. This way, the piRNApiRNA and diseasedisease subgraph were built using known PDAs.
It should be noted that piRNAs mostly point to only up to a few diseases. The default bipartite graph projection in the graph with a highly skewed degree distribution might void the assumption that piRNAs with similar functions are likely to be related to similar diseases. Specifically, for hub disease nodes in the piRNAdisease graph, a huge number, to the order of the square of disease degrees, of piRNApiRNA connections will be generated using the projection rule. To control the inflation, we proposed a sampling procedure based on median node centrality. In general, for data with a longtail distribution, the median and the region around it (e.g., the box in a boxplot) is appropriate to represent the data because the median is not sensitive to extremes. Instead of applying subgraph projection on all nodes, the projection is limited to certain number of nodes with centralities around the median. Therefore, we proposed a median centralitybased subsampling strategy as follows. For node centrality, we adopted the PageRank algorithm [29]. PageRank measures the importance of a node in relation to its linked nodes as following:
where, M_{i} is the set of all webpages that have links to webpage i, L(j) is the number of links out of webpage j, N is the total number of webpages and Î± is set as 0.85 by default.
In the piRNApiRNA/diseasedisease subgraph, we selected k nodes with PR values around the median PR. The value of k can be decided as follows:
where for piRNA projection subgraph, C_{pd} is the number of known PDAs. d_{max}/d_{min} denotes the maximum/minimum degree of diseases, respectively. Similarly, for disease projection subgraph, corresponding d_{max}/d_{min} denotes the maximum/minimum degree of piRNAs. Î¼ is a hyperparameter as dilution factor chosen from {1.0,10.0,100.0, 1000.0, â€¦}. Specifically, to limit k to an integer and in a reasonable range, Î¼ was tested at various values and set to 1.0 for disease subgraph and 100.0 for piRNA subgraph, respectively. The procedure of constructing piRNA/disease subgraph is presented as the following pseudocodes:
Node feature construction
Similarity features between entities are crucial to characterize the node primary representation and are complementary with topological structure information. To felicitously apply them, we took them as a type of similarity profile information for node feature and proposed a residual scalingbased feature augmentation algorithm to comprehend and enhance node diversity.
piRNA similarity profile
We adopted the Jaccard similarity coefficient [30] to calculate the kmer similarity profile [31] for piRNAs. We first obtained the kmer feature from original piRNA sequence information, where the k is empirically selected to 3(i.e.,3mer). The Jaccard similarity coefficient between x_{i} and x_{j} was calculated as follows:
where x_{i} and x_{j} denote the binary feature vector of entity i and j. \(x_{i} \cap x_{j} \) denotes the number of cases where both elements in x_{i} and x_{j} are equal to 1,and \(x_{i} \cup x_{j} \) denotes the number of the cases where either the elements of x_{i} or x_{j} are equal to 1.
Considering the influence of sparsity in piRNA similarity profile matrix, the sparse entries can be estimated by mathematical expectation of the similarity between Jaccard similarity coefficient and Gaussian interaction profile (GIP) kernel similarity [32]. The GIP similarity between piRNA i and j was calculated as follows:
where n_{d} is a factor used to control the bandwidth of kernel. We can calculate n_{d} by normalizing the original kernel bandwidth \(n^{{\prime }}_{d}\):
where k denotes the number of all diseases and \(n^{{\prime }}_{d}\) is usually set to 1. Thus, we can obtain the integrated piRNA similarity between i and j as follows:
Disease similarity profile
In PDAPRGCN, each disease including all related annotation terms obtained from MeSH descriptors can be represented by hierarchical directed acyclic graphs (DAGs). In general, a DAG can be expressed as DAGâ€‰=â€‰(T(d), E(d)), for a given disease d. T(d) denotes d itself together with all its ancestor nodes, while E(d) denotes all relationships between nodes in the DAG(d). D_{d}(t) of a disease t in a DAG to the semantics of disease d is defined as follows:
where \(\Delta\) is usually set to 0.5 according to previous studies. For a disease d to itself, the semantic contribution value is set 1, and with the distance between diseases increasing, the semantic contribution value will decrease. Thus, we can define the semantic value of disease d as following:
Following the method previously proposed [33], the semantic similarity score between disease i and j can be calculated by:
Similarly, with the same criteria as for piRNA, we can finally obtain the integrated disease similarity between i and j as follows:
Residual scalingbased node feature augmentation
In general, individual residual profile has different contributions in similarity matrix. Being a kind of localspecific residual, it could efficiently represent the proportions of each row and thus characterize the importance of similarity profile for each node. Therefore, for each row in piRNA/disease similarity profile matrix, we have:
where, S^{pp} (S^{dd} for disease) is the matrix of original similarity profile of piRNA, diag denotes the diagonalization operator. R^{i} denotes the residual profile and can be defined as following.
where, A^{pd} is the known PDA matrix. Here, we only considered the similarity of piRNA/disease distribution in ith row of S^{pp}. Î´ ^{i} is defined to describe the ratio of row degree distribution, while \(\Delta\)â€‰âˆˆâ€‰{0.1,10,100} denotes the scaling factor and is used to shrink or enlarge the difference of distribution among different piRNAs/diseases. Evidently, the nonnegative residuals are derived as R^{i} from Î´ ^{i} and mean degree Âµ. Here, ln(Â·) and Â· denotes the Napierian logarithm operator and the nonnegative absolute value operator, respectively. The procedure of residual scalingbased feature augmentation is presented as following pseudocodes (Here, the Stacked Autoencoder [34] was adopted to implement noise reduction.):
Graph convolution network
We adopted GCN to learn final embedding and finished the link prediction task by an endtoend mode. Specifically, we utilized the GCN with two convolution layers. The given graph adjacency matrix A and feature matrix H with the trainable weight vector W and the nonlinear activation function Ïƒ jointly define the neural network f (â‹…) as follows:
where \(\mathop {G = \, D}\nolimits^{(  1/2)} \,A^{{\prime }} D^{(  1/2)}\) with \(A^{{\prime }} = A + I\) and D is the diagonal degree matrix of \(A^{{\prime }}\), and ReLU is adopted as Ïƒ.
In view of the efficacy of PDA prediction, we designed a predictor of PDAPRGCN by applying a threelayer fullyconnected (FC) neural network to output the probability for potential links in the PDA graph, corresponding to feature extractor (i.e., GCN). Different from conventional dot product method [24, 35], our predictor is based on the endtoend mode aiming for a better integration of embeddings and joint optimization of the proposed model as well as downstream tasks.
Dualloss mechanism
Together with the crossentropy loss function applied to obtain the optimal classifications, the sensitivityâ€“specificity loss function [36] was jointly adopted to train PDAPRGCN. The dual loss is defined as follows.
where, \(y^{(i)}\) is the true label and \(\hat{y}^{(i)}\) is the predicted label. wâ€‰=â€‰{0.1,0.01} represents sensitivity ratio, which can control the balance between sensitivity and specificity. Herein, sp denotes \({\text{Specificity}} = \frac{{{\text{TN}}}}{{{\text{TN}} + {\text{FP}}}}\) and se denotes \({\text{Sensitivity}} = \frac{{{\text{TP}}}}{{{\text{TP}} + {\text{FN}}}} \,\) with FN, TN, TP and FP denoting false negative, true negative, true positive and false positive, respectively.
Availability of data and materials
All piRNAdisease association data in this study were downloaded from publicly available piRBase2.0 database, MNDR v3.1 database and piRDisease database, where the data provided by the three databases are open access and in accordance with the Declaration of Helsinki. Medical Subject Headings (MeSH) descriptor data of diseases were downloaded from https://meshb.nlm.nih.gov/. Source code of our models and training/testing datasets are available at: https://github.com/pzhangBIO/PDAPRGCN
Abbreviations
 ROC:

Receiver operating characteristic
 PR:

Precisionâ€“recall
 AUC:

Area under ROC curve
 AUPR:

Area under precisionâ€“recall curve
 DAG:

Directed acyclic graph
References
Seto AG, Kingston RE, Lau NC. The coming of age for Piwi proteins. Mol Cell. 2007;26:603â€“9.
Ozata DM, Gainetdinov I, Zoch A, Oâ€™Carroll D, Zamore PD. PIWIinteracting RNAs: small RNAs with big functions. Nat Rev Genet. 2019;20:89â€“108.
Qiu W, Guo X, Lin X, Yang Q, Zhang W, Zhang Y, et al. Transcriptomewide piRNA profiling in human brains of Alzheimerâ€™s disease. Neurobiol Aging. 2017;57:170â€“7.
Krishnan P, Ghosh S, Graham K, Mackey JR, Kovalchuk O, Damaraju S. Piwiinteracting RNAs and PIWI genes as novel prognostic markers for breast cancer. Oncotarget. 2016;7:37944â€“56.
Weng W, Liu N, Toiyama Y, Kusunoki M, Nagasaka T, Fujiwara T, et al. Novel evidence for a PIWIinteracting RNA (piRNA) as an oncogenic mediator of disease progression, and a potential prognostic biomarker in colorectal cancer. Mol Cancer. 2018;17:1â€“12.
Wang J, Zhang P, Lu Y, Li Y, Zheng Y, Kan Y, et al. PiRBase: a comprehensive database of piRNA sequences. Nucleic Acids Res. 2019;47:D175â€“80.
Muhammad A, Waheed R, Khan NA, Jiang H, Song X. PiRDisease v1.0: a manually curated database for piRNA associated diseases. Database. 2019;2019:baz052.
Zhang W, Song WU, Zhang H, Guan W, Zeng B, Wei Y, et al. piRPheno: a manually curated database to prioritize and analyze human disease related piRNAs. bioRxiv. 2020. https://doi.org/10.1101/2020.10.09.334219.
Ning L, Cui T, Zheng B, Wang N, Luo J, Yang B, et al. MNDR v3.0: mammal ncRNAdisease repository with increased coverage and annotation. Nucleic Acids Res. 2021;49:D160â€“4.
Wei H, Ding Y, Liu B. iPiDAsHN: identification of Piwiinteracting RNAdisease associations by selecting high quality negative samples. Comput Biol Chem. 2020;88:107361.
Wei H, Xu Y, Liu B. iPiDiPUL: identifying Piwiinteracting RNAdisease associations based on positive unlabeled learning. Brief Bioinform. 2021;22:bbaa058.
Zheng K, You ZH, Wang L, Wong L, Chen ZH. Inferring diseaseassociated Piwiinteracting RNAs via graph attention networks. In: International conference on intelligent computing. Springer; 2020. p. 239â€“50.
Zheng K, You ZH, Wang L, Wong L, Zhan ZH. SPRDA: a matrix completion approach based on the structural perturbation to infer diseaseassociated Piwiinteracting RNAs. bioRxiv. 2020;185611.
Kipf TN, Welling M. Semisupervised classification with graph convolutional networks. arXiv:1609.02907. 2016.
Van Der Maaten L, Hinton G. Visualizing data using tSNE. J Mach Learn Res. 2008;9:2579â€“625.
McInnes L, Healy J, Melville J. Umap: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426. 2018.
Zhou L, Wang Z, Tian X, Peng L. LPIdeepGBDT: a multiplelayer deep framework based on gradient boosting decision trees for lncRNAâ€“protein interaction identification. BMC Bioinform. 2021;22:1â€“24.
Ahmed NK, Rossi RA, Lee JB, Willke TL, Zhou R, Kong X, et al. role2vec: rolebased network embeddings. In: Proceedings of the first international workshop on deep learning for graphs (DLGâ€™19); 2019. pp. 1â€“7.
Zhao T, Hu Y, Valsdottir LR, Zang T, Peng J. Identifying drugâ€“target interactions based on graph convolutional network and deep neural network. Brief Bioinform. 2021;22:2141â€“50.
Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: predicting drugâ€“target binding affinity with graph neural networks. Bioinformatics. 2021;37:1140â€“7.
Wang S, Xu F, Li Y, Wang J, Zhang K, Liu Y, et al. KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers. Bioinformatics. 2021;37:i418â€“25.
Li J, Zhang S, Liu T, Ning C, Zhang Z, Zhou W. Neural inductive matrix completion with graph convolutional networks for miRNAdisease association prediction. Bioinformatics. 2020;36:2538â€“46.
Li J, Li Z, Nie R, You Z, Bao W. FCGCNMDA: predicting miRNAdisease associations by applying fully connected graph convolutional networks. Mol Genet Genomics. 2020;295:1197â€“209.
Yu Z, Huang F, Zhao X, Xiao W, Zhang W. Predicting drugâ€“disease associations through layer attention graph convolutional network. Brief Bioinform. 2021;22:bbaa243.
Grover A, Leskovec J. node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016. pp. 855â€“64.
Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q. LINE: largescale information network embedding. In: WWW 2015â€”proceedings of the 24th international conference on World Wide Web; 2015. pp. 1067â€“77.
Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016. pp. 1225â€“34.
Zhou T, Ren J, Medo M, Zhang YC. Bipartite network projection and personal recommendation. Phys Rev E Stat Nonlinear Soft Matter Phys. 2007;76:46115.
Pang X. The PageRank citation ranking: bring order to the web presented by: introduction. Inf Retr Boston. 2010; pp. 1â€“20.
Santisteban J, Tejada Carcamo JL. Unilateral Jaccard similarity coefficient. In: CEUR workshop proceedings; 2015. pp. 23â€“7.
Chor B, Horn D, Goldman N, Levy Y, Massingham T. Genomic DNA kmer spectra: models and modalities. Genome Biol. 2009;10:1â€“10.
Van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drugâ€“target interaction. Bioinformatics. 2011;27:3036â€“43.
Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNAassociated diseases. Bioinformatics. 2010;26:1644â€“50.
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res. 2010;11:3371â€“408.
Long Y, Wu M, Kwoh CK, Luo J, Li X. Predicting human microbeâ€“drug associations via graph convolutional network with conditional random field. Bioinformatics. 2020;36:4918â€“27.
Brosch T, Yoo Y, Tang LYW, Li DKB, Traboulsee A, Tam R. Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: International conference on medical image computing and computerassisted intervention. Springer; 2015. pp. 3â€“11.
Acknowledgements
The authors thank to lab members for assistance.
Funding
We acknowledge financial support from the Natural Science Foundation of Hubei Province (2021CFA061), HZAUAGIS Cooperation Found (SZYJY2022016) and Hubei Hongshan Laboratory Fund. Numerical computations were performed on the Hefei Advanced Computing Center.
Author information
Authors and Affiliations
Contributions
PZ and LL designed the methods and arranged the datasets. PZ and WS implemented the methods and performed the analyses. GL, JX, BZ, ZYÂ and DW tested the methods. PZ and LL wrote the manuscripts. DW and LL provided financial support and gave suggestions for improvement of the methods. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1.
Â Degree distribution statistics of nodes in the main dataset andÂ partial evaluation metrics definitions:Â Figure S1. Degree distribution of piRNAs in the main dataset; Figure S2. Degree distribution of diseases in main dataset; Note S1. Partial evaluation metrics definitions.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Zhang, P., Sun, W., Wei, D. et al. PDAPRGCN: identification of Piwiinteracting RNAdisease associations through subgraph projection and residual scalingbased feature augmentation. BMC Bioinformatics 24, 18 (2023). https://doi.org/10.1186/s12859022050733
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12859022050733
Keywords
 Piwiinteracting RNA
 Disease
 Subgraph projection
 Feature augmentation
 Graph convolutional network
 piRNAdisease associations