Dataset Information

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy.

ABSTRACT:

Background

CircRNAs are essential for the regulation of post-transcriptional gene expression, including as miRNA sponges, and play an important role in disease development. Some computational tools have been proposed recently to predict circRNA, since only one classifier is used, there is still much that can be done to improve the performance.

Results

StackCirRNAPred was proposed, the computational classification of long circRNA from other lncRNA based on stacking strategy. In order to cope with the potential problem that a single feature might not be able to distinguish circRNA well from other lncRNA, we first extracted features from different sources, including nucleic acid composition, sequence spatial features and physicochemical properties, Alu and tandem repeats. We innovatively apply the stacking strategy to integrate the more advantageous classifiers of RF, LightGBM, XGBoost. This allows the model to incorporate these features more flexibly. StackCirRNAPred was found to be significantly better than other tools, with precision, accuracy, F1, recall and MCC of 0.843, 0.833, 0.831, 0.819 and 0.666 respectively. We tested it directly on the mouse dataset. StackCirRNAPred was still significantly better than other methods, with precision, accuracy, F1, recall and MCC of 0.837, 0.839, 0.839, 0.841, 0.677.

Conclusions

We proposed StackCirRNAPred based on stacking strategy to distinguish long circRNAs from other lncRNAs. With the test results demonstrating the validity and robustness of StackCirRNAPred, we hope StackCirRNAPred will complement existing circRNA prediction methods and is helpful in down-stream research.

SUBMITTER: Wang X

PROVIDER: S-EPMC9793644 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy.

Wang Xin X Liu Yadong Y Li Jie J Wang Guohua G

BMC bioinformatics 20221227 1

<h4>Background</h4>CircRNAs are essential for the regulation of post-transcriptional gene expression, including as miRNA sponges, and play an important role in disease development. Some computational tools have been proposed recently to predict circRNA, since only one classifier is used, there is still much that can be done to improve the performance.<h4>Results</h4>StackCirRNAPred was proposed, the computational classification of long circRNA from other lncRNA based on stacking strategy. In ord ...[more]

PMID: 36575368

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Dataset Information

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy.

Background

Results

Conclusions

Publications

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy.

Similar Datasets

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