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Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.

Shao M, Sun Y, Zhou S - PLoS ONE (2015)

Bottom Line: To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks.We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells.The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China; Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, USA.

ABSTRACT
MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

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The recall and removal rate of prediction on Esrrb-miRNA relationships using protein-coding gene related positive data sets of transcription factor Esrrb and five-fold cross validation.In each panel, the x-axis denotes the parameter p of SVMlight, it ranges from 0.05 to 0.95 with a step size of 0.05. The y-axis denotes the recall (left panel) and the removal rate (right panel) of the prediction, respectively.
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pone.0125156.g006: The recall and removal rate of prediction on Esrrb-miRNA relationships using protein-coding gene related positive data sets of transcription factor Esrrb and five-fold cross validation.In each panel, the x-axis denotes the parameter p of SVMlight, it ranges from 0.05 to 0.95 with a step size of 0.05. The y-axis denotes the recall (left panel) and the removal rate (right panel) of the prediction, respectively.

Mentions: Fig 6 demonstrates the recall and the removal rate with different p values using five-fold cross validation for the Esrrb transcription factor (the results for the other four TFs can refer to Fig. A–D in S1 Text). We can see from the left panel of the figure that the recall is generally high with different p values. In order to further determine the performance of the classifier, we also computed the removal rate of unlabeled data. From the right panel, we can see that quite a portion of unlabeled data are predicted as negatives. This indicates that the classifier can retrieve almost all positives while still removing possible negatives.


Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.

Shao M, Sun Y, Zhou S - PLoS ONE (2015)

The recall and removal rate of prediction on Esrrb-miRNA relationships using protein-coding gene related positive data sets of transcription factor Esrrb and five-fold cross validation.In each panel, the x-axis denotes the parameter p of SVMlight, it ranges from 0.05 to 0.95 with a step size of 0.05. The y-axis denotes the recall (left panel) and the removal rate (right panel) of the prediction, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4414601&req=5

pone.0125156.g006: The recall and removal rate of prediction on Esrrb-miRNA relationships using protein-coding gene related positive data sets of transcription factor Esrrb and five-fold cross validation.In each panel, the x-axis denotes the parameter p of SVMlight, it ranges from 0.05 to 0.95 with a step size of 0.05. The y-axis denotes the recall (left panel) and the removal rate (right panel) of the prediction, respectively.
Mentions: Fig 6 demonstrates the recall and the removal rate with different p values using five-fold cross validation for the Esrrb transcription factor (the results for the other four TFs can refer to Fig. A–D in S1 Text). We can see from the left panel of the figure that the recall is generally high with different p values. In order to further determine the performance of the classifier, we also computed the removal rate of unlabeled data. From the right panel, we can see that quite a portion of unlabeled data are predicted as negatives. This indicates that the classifier can retrieve almost all positives while still removing possible negatives.

Bottom Line: To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks.We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells.The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China; Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, USA.

ABSTRACT
MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

Show MeSH