SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost
| Autor: | Jiaxuan Zhang, Wenjuan Nie, Dayun Liu, Lei Deng, Yibiao Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
QH301-705.5 Feature vector Computer applications to medicine. Medical informatics R858-859.7 Breast Neoplasms Disease Computational biology Latent feature Biochemistry 03 medical and health sciences 0302 clinical medicine Molecular level Semantic similarity Structural Biology microRNA Humans Biology (General) miRNA-disease associations Molecular Biology 030304 developmental biology 0303 health sciences Applied Mathematics Research Computational Biology Autoencoder Stacked autoencoder Computer Science Applications MicroRNAs Feature (computer vision) 030220 oncology & carcinogenesis DNA microarray Algorithms XGBoost |
| Zdroj: | BMC Bioinformatics BMC Bioinformatics, Vol 22, Iss 1, Pp 1-18 (2021) |
| ISSN: | 1471-2105 |
| Popis: | Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink