Predicting circRNA-disease Associations Research Articles

GEHGAN[formula omitted] CircRNA–disease association prediction via graph embedding and heterogeneous graph attention network

There is growing proof suggested that circRNAs play a crucial function in diverse important biological reactions related to human diseases. Within the area of biochemistry, a massive range of wet experiments have been carried out to find out the connections of circRNA–disease in recent years. Since wet experiments are expensive and laborious, nowadays, calculation-based solutions have increasingly attracted the attention of researchers. However, the performance of these methods is restricted due to the inability to balance the distribution among various types of nodes. To remedy the problem, we present a novel computational method called GEHGAN to forecast the new relationships in this research, leveraging graph embedding and heterogeneous graph attention networks. Firstly, we calculate circRNA sequences similarity, circRNA RBP similarity, disease semantic similarity and corresponding GIP kernel similarity to construct heterogeneous graph. Secondly, a graph embedding method using random walks with jump and stay strategies is applied to obtain the preliminary embeddings of circRNAs and diseases, greatly improving the performance of the model. Thirdly, a multi-head graph attention network is employed to further update the embeddings, followed by the employment of the MLP as a predictor. As a result, the five-fold cross-validation indicates that GEHGAN achieves an outstanding AUC score of 0.9829 and an AUPR value of 0.9815 on the CircR2Diseasev2.0 database, and case studies on osteosarcoma, gastric and colorectal neoplasms further confirm the model’s efficacy at identifying circRNA–disease correlations.

Identification of circRNA-disease associations via multi-model fusion and ensemble learning.

Circular RNA (circRNA) is a common non-coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA-disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. In this article, we proposed a novel method for circRNA-disease associations prediction based on ensemble learning, named ELCDA. First, the association heterogeneous network was constructed via collecting multiple information of circRNAs and diseases, and multiple similarity measures are adopted here, then, we use metapath, matrix factorization and GraphSAGE-based models to extract features of nodes from different views, the final comprehensive features of circRNAs and diseases via ensemble learning, finally, a soft voting ensemble strategy is used to integrate the predicted results of all classifier. The performance of ELCDA is evaluated by fivefold cross-validation and compare with other state-of-the-art methods, the experimental results show that ELCDA is outperformance than others. Furthermore, three common diseases are used as case studies, which also demonstrate that ELCDA is an effective method for predicting circRNA-disease associations.

Bi-SGTAR: A simple yet efficient model for circRNA-disease association prediction based on known association pair only

Identifying circRNA (circular RNA) associated with diseases holds promise as diagnostic and prognostic biomarkers, offering potential avenues for novel therapeutics. Several computational methods have been designed to predict circRNA-disease associations. Unfortunately, current computational models face issues stemming from the integration of data from multiple sources, leading to blind spots in data combination and increased model complexity. Thus, this article introduces a novel method named Bi-SGTAR (Bi-view Sparse Gating and True Association Regression). Notably, Bi-SGTAR demonstrates comparable performance to existing multi-source information fusion methods while utilizing only known circRNA-disease association pairs. In contrast to previous methods, the model divides the adjacency matrix into two views and employs an encoder with sparse gating to assess the reliability of all associations. Additionally, a supervised reconstructor is employed to define the true association probability, quantifying the truthfulness of all associations. The Encoding-Reconstruction-Regression (ERR) framework adeptly merges both reliable and truthful associations from both views. The experimental results unequivocally show that Bi-SGTAR surpasses state-of-the-art models across seven circRNA-disease datasets, one lncRNA-disease dataset, and one microbe-drug dataset, with fewer data needed.

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

With a growing body of evidence establishing circular RNAs (circRNAs) are widely exploited in eukaryotic cells and have a significant contribution in the occurrence and development of many complex human diseases. Disease-associated circRNAs can serve as clinical diagnostic biomarkers and therapeutic targets, providing novel ideas for biopharmaceutical research. However, available computation methods for predicting circRNA-disease associations (CDAs) do not sufficiently consider the contextual information of biological network nodes, making their performance limited. In this work, we propose a multi-hop attention graph neural network-based approach MAGCDA to infer potential CDAs. Specifically, we first construct a multi-source attribute heterogeneous network of circRNAs and diseases, then use a multi-hop strategy of graph nodes to deeply aggregate node context information through attention diffusion, thus enhancing topological structure information and mining data hidden features, and finally use random forest to accurately infer potential CDAs. In the four gold standard data sets, MAGCDA achieved prediction accuracy of 92.58%, 91.42%, 83.46% and 91.12%, respectively. MAGCDA has also presented prominent achievements in ablation experiments and in comparisons with other models. Additionally, 18 and 17 potential circRNAs in top 20 predicted scores for MAGCDA prediction scores were confirmed in case studies of the complex diseases breast cancer and Almozheimer's disease, respectively. These results suggest that MAGCDA can be a practical tool to explore potential disease-associated circRNAs and provide a theoretical basis for disease diagnosis and treatment.

LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.

CircRNA-disease association prediction with an improved unbalanced Bi-Random walk

Studies show that circular RNAs (circRNAs), a type of non-coding RNAs, play various roles in biological processes such as the formation and progression of many different diseases. For this reason, identifying potential circRNAs associated with diseases is vital for early diagnosis. Determining these relationships experimentally requires a long process and is also expensive. For this reason, computational models are being developed to determine the relationships between circRNA and diseases. In this study, we recommend a technique called Improved Unbalanced Bi-Random Walk (UBRW) to identify potential circRNAs associated with diseases. The commonly used 5-fold cross-validation (CV) technique and leave one-out cross-validation (LOOCV) technique were applied to verify the predictive ability of our technique. The area under curve (AUC) values calculated in 5-fold CV and LOOCV are 0.8910 and 0.9669, respectively. Case studies on the commonly occurring gastric cancer and breast cancer were conducted to further validate the predictive performance of our method. When the results were examined, it was seen that the prediction ability of the UBRW method was quite successful.

LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five- fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods. The code is available at https://github.com/lanbiolab/LGCDA.

DETHACDA: A Dual-view Edge and Topology Hybrid Attention Model for CircRNA-Disease Associations Prediction.

There exists growing evidence that circRNAs are concerned with many complex diseases physiological processes and pathogenesis and may serve as critical therapeutic targets. Identifying disease-associated circRNAs through biological experiments is time-consuming, and designing an intelligent, precise calculation model is essential. Recently, many models based on graph technology have been proposed to predict circRNA-disease association. However, most existing methods only capture the neighborhood topology of the association network and ignore the complex semantic information. Therefore, we propose a Dual-view Edge and Topology Hybrid Attention model for predicting CircRNA-Disease Associations (DETHACDA), effectively capturing the neighborhood topology and various semantics of circRNA and disease nodes in a heterogeneous network. The 5-fold cross-validation experiments on circRNADisease indicate that the proposed DETHACDA achieves the area under receiver operating characteristic curve of 0.9882, better than four state-of-the-art calculation methods.

RDGAN: Prediction of circRNA-Disease Associations Via Resistance Distance and Graph Attention Network.

As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort. Therefore, various computational methods have been proposed to address this limitation, but how to extract features more comprehensively remains a challenge that needs further attention in the future. In this study, we propose a unique approach to predict circRNA-disease associations based on resistance distance and graph attention network (RDGAN). Firstly, the associations of circRNA and disease are obtained by fusing multiple databases, and resistance distance as a similarity matrix is used to further deal with the sparse of the similarity matrices. Then the circRNA-disease heterogeneous network is constructed based on the similiarity of circRNA-circRNA, disease-disease and the known circRNA-disease adjacency matric. Secondly, leveraging the three neural network modules-ResGatedGraphConv, GAT and MFConv-we gather node feature embeddings collected from the heterogeneous network. Subsequently, all the characteristics are supplied to the self-attention mechanism to predict new potential connections. Finally, our model obtains a remarkable AUC value of 0.9630 through five- fold cross-validation, surpassing the predictive performance of the other eight state-of-the-art models.

Tensor product graph diffusion based on nonlinear fusion of multi-source information to predict circRNA-disease associations

Circular RNA (circRNA), a non-coding RNA, has been shown in recent years via numerous studies to be more than just a "by-product" of gene transcription. It also participates in the regulation of many diseases and influences various biological functions in the human body. Its unique properties and biological functions indicate that circRNA may become new biomarker and therapeutic target, thus, it is crucial for modern medicine to identify potentially relevant circRNAs. This paper proposed novel computational model SNFTPGd-CDA for circRNA-disease associations prediction with the CircR2Disease v2.0 as dataset, which first constructs multiple similarity networks according to multi-source information related to circRNA and disease, and uses similarity network fusion (SNF) to nonlinearly fuse multi-source similarity networks. Then tensor product graph diffusion (TPGd) is employed to diffuse the similarity in the tensor product graph of the fused similarity network into the context of other nodes, which in turn constructs a more reliable similarity network as the feature matrix. Finally, the cascaded forest model is employed for circRNA-disease associations prediction. An AUC value of 0.9937 and an accuracy of 96.92 % were obtained with five-fold cross-validation. This result indicates that SNFTPGd-CDA achieves more accurate performance than previous methods, and can support validation of circRNA-disease associations.

AMPCDA: Prediction of circRNA–disease associations by utilizing attention mechanisms on metapaths

Researchers have been creating an expanding corpus of experimental evidences in biomedical field which has revealed prevalent associations between circRNAs and human diseases. Such linkages unveiled afforded a new perspective for elucidating etiology and devise innovative therapeutic strategies. In recent years, many computational methods were introduced to remedy the limitations of inefficiency and exorbitant budgets brought by conventional lab-experimental approaches to enumerate possible circRNA–disease associations, but the majority of existing methods still face challenges in effectively integrating node embeddings with higher-order neighborhood representations, which might hinder the final predictive accuracy from attaining optimal measures. To overcome such constraints, we proposed AMPCDA, a computational technique harnessing predefined metapaths to predict circRNA–disease associations. Specifically, an association graph is initially built upon three source databases and two similarity derivation procedures, and DeepWalk is subsequently imposed on the graph to procure initial feature representations. Vectorial embeddings of metapath instances, concatenated by initial node features, are then fed through a customed encoder. By employing self-attention section, metapath-specific contributions to each node are accumulated before combining with node’s intrinsic features and channeling into a graph attention module, which furnished the input representations for the multilayer perceptron to predict the ultimate association probability scores. By integrating graph topology features and node embedding themselves, AMPCDA managed to effectively leverage information carried by multiple nodes along paths and exhibited an exceptional predictive performance, achieving AUC values of 0.9623, 0.9675, and 0.9711 under 5-fold cross validation, 10-fold cross validation, and leave-one-out cross validation, respectively. These results signify substantial accuracy improvements compared to other prediction models. Case study assessments confirm the high predictive accuracy of our proposed technique in identifying circRNA–disease connections, highlighting its value in guiding future biological research to reveal new disease mechanisms.

HGECDA: A Heterogeneous Graph Embedding Model for CircRNA-Disease Association Prediction.

Circular RNAs (circRNAs) are specifically and abnormally expressed in disease tissues, and thus can be used as biomarkers to diagnose relevant diseases. Predicting circRNA-disease associations will provide essential clues to reveal molecular mechanisms of disease development and discover novel therapeutic targets. Existing algorithms ignore the heterogeneous biological association information related to microRNAs (miRNAs). Based on a heterogeneous graph embedding model, a novel circRNA-disease association prediction method called HGECDA is developed in this paper. The heterogeneous graph network containing circRNA-miRNA-disease association information is first constructed. To sample the heterogeneous information, the meta-path-based random walk that can capture the relevance between various types of nodes is employed. Then, the path embedding model based on skip-gram and random negative sampling is built to acquire the initial feature vectors of circRNAs and diseases. Finally, the CosMulformer model with linearized self-attention and Hadamard product is designed to obtain the circRNA-disease interaction vectors and conduct the prediction task. Experimental results demonstrate the critical role of miRNA in enriching the information of the feature space, the effectiveness of the CosMulformer model in picking out deep local interaction features, and the feasibility of the Hadamard product chosen as the integration pattern in the CosMulformer model. Compared with existing state-of-the-art methods on the same dataset, HGECDA performs better than the other seven algorithms. Moreover, the case studies about breast cancer and colorectal cancer demonstrate the practical value of HGECDA in predicting potential circRNA-disease associations.

THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.

Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of disease have revealed that circRNA can be considered as a potential biomarker. Therefore, understanding the association of circRNA and diseases can help to forecast some disorders of life activities. However, traditional biological experimental methods are time-consuming. The most common method for circRNA-disease association prediction on the basis of machine learning can avoid this, which relies on diverse data. Nevertheless, topological information of circRNA and disease usually is not involved in these methods. Moreover, circRNAs can be associated with diseases through miRNAs. With these considerations, we proposed a novel method, named THGNCDA, to predict the association between circRNAs and diseases. Specifically, for a certain pair of circRNA and disease, we employ a graph neural network with attention to learn the importance of its each neighbor. In addition, we use a multilayer convolutional neural network to explore the relationship of a circRNA-disease pair based on their attributes. When calculating embeddings, we introduce the information of miRNAs. The results of experiments show that THGNCDA outperformed the SOTA methods. In addition, it can be observed that our method gives a better recall rate. To confirm the significance of attention, we conducted extensive ablation studies. Case studies on Urinary Bladder and Prostatic Neoplasms further show THGNCDA's ability in discovering known relationships between circRNA candidates and diseases.

ICircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction.

Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations.

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations.

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at https://github.com/jinyangwu/KGETCDA.

MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.

Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborious. Meanwhile, computational models can effectively predict potential circRNA-disease associations (CDAs), but are restricted by limited data, resulting in data with high dimension and imbalance. In this study, we propose a model based on automatically selected meta-path and contrastive learning, called the MPCLCDA model. First, the model constructs a new heterogeneous network based on circRNA similarity, disease similarity and known association, via automatically selected meta-path and obtains the low-dimensional fusion features of nodes via graph convolutional networks. Then, contrastive learning is used to optimize the fusion features further, and obtain the node features that make the distinction between positive and negative samples more evident. Finally, circRNA-disease scores are predicted through a multilayer perceptron. The proposed method is compared with advanced methods on four datasets. The average area under the receiver operating characteristic curve, area under the precision-recall curve and F1 score under 5-fold cross-validation reached 0.9752, 0.9831 and 0.9745, respectively. Simultaneously, case studies on human diseases further prove the predictive ability and application value of this method.

Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network.

Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing the mechanisms of disease pathogenesis. However, a blind search for all possible associations between circRNAs and diseases through biological experiments is time-consuming. Although some prediction methods have been proposed, they still have limitations. In this study, a novel computational framework, called GATCL2CD, is proposed to forecast unknown circRNA-disease associations (CDAs). First, we calculate Gaussian interactive profile kernel (GIP) similarity and semantic similarity for diseases, circRNA sequence similarity and function similarity, and GIPs for circRNAs. Then, we combine them to construct a heterogeneous graph. Thereafter, GATCL2CD proposes a feature convolution learning framework, that uses a multi-head dynamic attention mechanism to obtain different aggregated representations of features that correspond to the nodes in the heterogeneous graph. Then, it extracts rich higher-order features from the stacked feature representations of each node by using of a single-layer convolutional neural network with filter kernels of different sizes. Finally, a pairwise element-wise product operation is implemented to capture the interactions of higher-order feature representations, and a multilayer perceptron neural network is introduced as an efficient classifier for inferring potential CDAs. Major experimental results under 5-fold cross-validation (5-fold CV) on three different datasets show that GATCL2CD is superior to five other state-of-the-art methods. Furthermore, case studies demonstrate the suitability of GATCL2CD as a useful tool for identifying potential disease-related circRNAs.

Benchmarking of computational methods for predicting circRNA-disease associations

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder.

Circular RNAs (circRNAs) constitute a category of circular non-coding RNA molecules whose abnormal expression is closely associated with the development of diseases. As biological data become abundant, a lot of computational prediction models have been used for circRNA-disease association prediction. However, existing prediction models ignore the non-linear information of circRNAs and diseases when fusing multi-source similarities. In addition, these models fail to take full advantage of the vital feature information of high-similarity neighbor nodes when extracting features of circRNAs or diseases. In this paper, we propose a deep learning model, CDA-SKAG, which introduces a similarity kernel fusion algorithm to integrate multi-source similarity matrices to capture the non-linear information of circRNAs or diseases, and construct a circRNA information space and a disease information space. The model embeds an attention-enhancing layer in the graph autoencoder to enhance the associations between nodes with higher similarity. A cost-sensitive neural network is introduced to address the problem of positive and negative sample imbalance, consequently improving our model's generalization capability. The experimental results show that the prediction performance of our model CDA-SKAG outperformed existing circRNA-disease association prediction models. The results of the case studies on lung and cervical cancer suggest that CDA-SKAG can be utilized as an effective tool to assist in predicting circRNA-disease associations.

GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.

Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated with the pathogenesis of numerous diseases. Clarification of the correlation among diseases and circRNAs is of great clinical importance to provide new therapeutic strategies for complex diseases. However, previous circRNA-disease association prediction methods rely excessively on the graph network, and the model performance is dramatically reduced when noisy connections occur in the graph structure. To address this problem, this paper proposes an unsupervised deep graph structure learning method GSLCDA to predict potential CDAs. Concretely, we first integrate circRNA and disease multi-source data to constitute the CDA heterogeneous network. Then the network topology is learned using the graph structure, and the original graph is enhanced in an unsupervised manner by maximize the inter information of the learned and original graphs to uncover their essential features. Finally, graph space sensitive k-nearest neighbor (KNN) algorithm is employed to search for latent CDAs. In the benchmark dataset, GSLCDA obtained 92.67% accuracy with 0.9279 AUC. GSLCDA also exhibits exceptional performance on independent datasets. Furthermore, 14, 12 and 14 of the top 16 circRNAs with the most points GSLCDA prediction scores were confirmed in the relevant literature in the breast cancer, colorectal cancer and lung cancer case studies, respectively. Such results demonstrated that GSLCDA can validly reveal underlying CDA and offer new perspectives for the diagnosis and therapy of complex human diseases.