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miSTAR : miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure

Gert Van Peer (UGent) , Ayla De Paepe, Michiel Stock (UGent) , Jasper Anckaert (UGent) , Pieter-Jan Volders (UGent) , Jo Vandesompele (UGent) , Bernard De Baets (UGent) and Willem Waegeman (UGent)
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Abstract
In microRNA (miRNA) target prediction, typically two levels of information need to be modeled: the number of potential miRNA binding sites present in a target mRNA and the genomic context of each individual site. Single model structures insufficiently cope with this complex training data structure, consisting of feature vectors of unequal length as a consequence of the varying number of miRNA binding sites in different mRNAs. To circumvent this problem, we developed a two-layered, stacked model, in which the influence of binding site context is separately modeled. Using logistic regression and random forests, we applied the stacked model approach to a unique data set of 7990 probed miRNA-mRNA interactions, hereby including the largest number of miRNAs in model training to date. Compared to lower-complexity models, a particular stacked model, named miSTAR (miRNA stacked model target prediction; www.mi-star.org), displays a higher general performance and precision on top scoring predictions. More importantly, our model outperforms published and widely used miRNA target prediction algorithms. Finally, we highlight flaws in cross-validation schemes for evaluation of miRNA target prediction models and adopt a more fair and stringent approach.
Keywords
MICRORNA TARGETS, IDENTIFICATION, REPRESSION, CURVES

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MLA
Van Peer, Gert et al. “miSTAR : miRNA Target Prediction Through Modeling Quantitative and Qualitative miRNA Binding Site Information in a Stacked Model Structure.” NUCLEIC ACIDS RESEARCH 45.7 (2017): n. pag. Print.
APA
Van Peer, G., De Paepe, A., Stock, M., Anckaert, J., Volders, P.-J., Vandesompele, J., De Baets, B., et al. (2017). miSTAR : miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure. NUCLEIC ACIDS RESEARCH, 45(7).
Chicago author-date
Van Peer, Gert, Ayla De Paepe, Michiel Stock, Jasper Anckaert, Pieter-Jan Volders, Jo Vandesompele, Bernard De Baets, and Willem Waegeman. 2017. “miSTAR : miRNA Target Prediction Through Modeling Quantitative and Qualitative miRNA Binding Site Information in a Stacked Model Structure.” Nucleic Acids Research 45 (7).
Chicago author-date (all authors)
Van Peer, Gert, Ayla De Paepe, Michiel Stock, Jasper Anckaert, Pieter-Jan Volders, Jo Vandesompele, Bernard De Baets, and Willem Waegeman. 2017. “miSTAR : miRNA Target Prediction Through Modeling Quantitative and Qualitative miRNA Binding Site Information in a Stacked Model Structure.” Nucleic Acids Research 45 (7).
Vancouver
1.
Van Peer G, De Paepe A, Stock M, Anckaert J, Volders P-J, Vandesompele J, et al. miSTAR : miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure. NUCLEIC ACIDS RESEARCH. 2017;45(7).
IEEE
[1]
G. Van Peer et al., “miSTAR : miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure,” NUCLEIC ACIDS RESEARCH, vol. 45, no. 7, 2017.
@article{8509343,
  abstract     = {In microRNA (miRNA) target prediction, typically two levels of information need to be modeled: the number of potential miRNA binding sites present in a target mRNA and the genomic context of each individual site. Single model structures insufficiently cope with this complex training data structure, consisting of feature vectors of unequal length as a consequence of the varying number of miRNA binding sites in different mRNAs. To circumvent this problem, we developed a two-layered, stacked model, in which the influence of binding site context is separately modeled. Using logistic regression and random forests, we applied the stacked model approach to a unique data set of 7990 probed miRNA-mRNA interactions, hereby including the largest number of miRNAs in model training to date. Compared to lower-complexity models, a particular stacked model, named miSTAR (miRNA stacked model target prediction; www.mi-star.org), displays a higher general performance and precision on top scoring predictions. More importantly, our model outperforms published and widely used miRNA target prediction algorithms. Finally, we highlight flaws in cross-validation schemes for evaluation of miRNA target prediction models and adopt a more fair and stringent approach.},
  articleno    = {e51},
  author       = {Van Peer, Gert and De Paepe, Ayla and Stock, Michiel and Anckaert, Jasper and Volders, Pieter-Jan and Vandesompele, Jo and De Baets, Bernard and Waegeman, Willem},
  issn         = {0305-1048},
  journal      = {NUCLEIC ACIDS RESEARCH},
  keywords     = {MICRORNA TARGETS,IDENTIFICATION,REPRESSION,CURVES},
  language     = {eng},
  number       = {7},
  pages        = {9},
  title        = {miSTAR : miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure},
  url          = {http://dx.doi.org/10.1093/nar/gkw1260},
  volume       = {45},
  year         = {2017},
}

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