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• W2108206849 startingPage "2219" @default.
• W2108206849 abstract "Recently circular RNA (circularRNA) has been discovered as an increasingly important type of long non-coding RNA (lncRNA), playing an important role in gene regulation, such as functioning as miRNA sponges. So it is very promising to identify circularRNA transcripts from de novo assembled transcripts obtained by high-throughput sequencing, such as RNA-seq data. In this study, we presented a machine learning approach, named as PredcircRNA, focused on distinguishing circularRNA from other lncRNAs using multiple kernel learning. Firstly we extracted different sources of discriminative features, including graph features, conservation information and sequence compositions, ALU and tandem repeats, SNP densities and open reading frames (ORFs) from transcripts. Secondly, to better integrate features from different sources, we proposed a computational approach based on a multiple kernel learning framework to fuse those heterogeneous features. Our preliminary 5-fold cross-validation result showed that our proposed method can classify circularRNA from other types of lncRNAs with an accuracy of 0.778, sensitivity of 0.781, specificity of 0.770, precision of 0.784 and MCC of 0.554 in our constructed gold-standard dataset, respectively. Our feature importance analysis based on Random Forest illustrated some discriminative features, such as conservation features and a GTAG sequence motif. Our PredcircRNA tool is available for download at ." @default.
• W2108206849 created "2016-06-24" @default.
• W2108206849 creator A5064570295 @default.
• W2108206849 creator A5064600744 @default.
• W2108206849 date "2015-01-01" @default.
• W2108206849 modified "2023-09-27" @default.
• W2108206849 title "PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features" @default.
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• W2108206849 doi "https://doi.org/10.1039/c5mb00214a" @default.
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• W2108206849 hasPublicationYear "2015" @default.
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