A Bayesian decision fusion approach for microRNA target prediction
<p>Abstract</p> <p>MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely...
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2012-12-01
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| Series: | BMC Genomics |
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doaj-eadddf452b3c4f9482b3182329d1c95d2020-11-24T23:07:47ZengBMCBMC Genomics1471-21642012-12-0113Suppl 8S1310.1186/1471-2164-13-S8-S13A Bayesian decision fusion approach for microRNA target predictionYue DongGuo MaozuChen YidongHuang Yufei<p>Abstract</p> <p>MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at <url>http://compgenomics.utsa.edu/gene/gene_1.php</url>.</p> |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Yue Dong Guo Maozu Chen Yidong Huang Yufei |
| spellingShingle |
Yue Dong Guo Maozu Chen Yidong Huang Yufei A Bayesian decision fusion approach for microRNA target prediction BMC Genomics |
| author_facet |
Yue Dong Guo Maozu Chen Yidong Huang Yufei |
| author_sort |
Yue Dong |
| title |
A Bayesian decision fusion approach for microRNA target prediction |
| title_short |
A Bayesian decision fusion approach for microRNA target prediction |
| title_full |
A Bayesian decision fusion approach for microRNA target prediction |
| title_fullStr |
A Bayesian decision fusion approach for microRNA target prediction |
| title_full_unstemmed |
A Bayesian decision fusion approach for microRNA target prediction |
| title_sort |
bayesian decision fusion approach for microrna target prediction |
| publisher |
BMC |
| series |
BMC Genomics |
| issn |
1471-2164 |
| publishDate |
2012-12-01 |
| description |
<p>Abstract</p> <p>MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at <url>http://compgenomics.utsa.edu/gene/gene_1.php</url>.</p> |
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