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LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection
Yi Zhang1, Min Chen2, Xiaohui Cheng1
1School of Information Science and Engineering, Guilin University of Technology 541004 Guilin China.
Abstract:
Lots of research findings have indicated that miRNAs (microRNAs) are involved in many important biological processes; their mutations and disorders are closely related to diseases, therefore, determining the associations between human diseases and miRNAs is key to understand pathogenic mechanisms. Existing biological experimental methods for identifying miRNA-disease associations are usually expensive and time consuming. Therefore, the development of efficient and reliable computational methods for identifying disease-related miRNAs has become an important topic in the field of biological research in recent years. In this study, we developed a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method (LSGSP). This integrates experimentally validated miRNA-disease associations, disease semantic similarity scores, miRNA functional scores, and miRNA family information to build a new disease similarity network and miRNA similarity network, and then obtains the global similarities of these networks through calculating the Laplacian score of the graphs, based on which the miRNA-disease weighted network can be constructed through combination with the miRNA-disease Boolean network. Finally, the miRNA-disease score was obtained via projecting the miRNA space and disease space onto the miRNA-disease weighted network. Compared with several other state-of-the-art methods, using leave-one-out cross validation (LOOCV) to evaluate the accuracy of LSGSP with respect to a benchmark dataset, prediction dataset and compare dataset, LSGSP showed excellent predictive performance with high AUC values of 0.9221, 0.9745 and 0.9194, respectively. In addition, for prostate neoplasms and lung neoplasms, the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the updated HMDD, miR2Disease, and dbDEMC databases) reached 96% and 100%, respectively. Similarly, for isolated diseases (diseases not associated with any miRNAs), the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the above-mentioned three databases) reached 98% and 100%, respectively. These results further indicate that LSGSP can effectively predict potential associations between miRNAs and diseases.
Insights
This study introduces LSGSP, a computational model for predicting microRNA (miRNA)-disease associations. LSGSP effectively identifies potential links between miRNAs and diseases, aiding in understanding disease mechanisms.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial in biological processes, and their dysregulation is linked to various diseases.
- Experimental identification of miRNA-disease associations is costly and time-consuming.
- Developing efficient computational methods for miRNA-disease association prediction is essential.
Purpose of the Study:
- To develop a novel computational model, LSGSP, for predicting associations between human diseases and miRNAs.
- To improve the accuracy and efficiency of identifying disease-related miRNAs.
Main Methods:
- Developed a Laplacian score of graphs and space projection federated method (LSGSP).
- Integrated miRNA-disease associations, disease semantic similarity, miRNA functional scores, and miRNA family information.
- Constructed novel miRNA and disease similarity networks, and a weighted miRNA-disease network.
- Utilized space projection onto the weighted network to derive miRNA-disease scores.
Main Results:
- LSGSP demonstrated excellent predictive performance with high AUC values (0.9221, 0.9745, 0.9194) in cross-validation.
- High consistency (96-100%) was observed between LSGSP predictions and database-confirmed associations for specific neoplasms and isolated diseases.
Conclusions:
- LSGSP is an effective computational tool for predicting potential miRNA-disease associations.
- The model aids in understanding disease pathogenic mechanisms by identifying novel miRNA-disease links.
- LSGSP offers a more efficient alternative to experimental methods for miRNA-disease association discovery.
