Dataset Information

Prediction of circRNA-disease associations based on inductive matrix completion.

ABSTRACT:

Background

Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies.

Results

Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation.

Conclusion

All the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.

SUBMITTER: Li M

PROVIDER: S-EPMC7118830 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Prediction of circRNA-disease associations based on inductive matrix completion.

Li Menglu M Liu Mengya M Bin Yannan Y Xia Junfeng J

BMC medical genomics 20200403 Suppl 5

<h4>Background</h4>Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies.<h4>Results</h4>Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease as ...[more]

PMID: 32241268

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Dataset Information

Prediction of circRNA-disease associations based on inductive matrix completion.

Background

Results

Conclusion

Publications

Prediction of circRNA-disease associations based on inductive matrix completion.

Similar Datasets

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