MOTIVATION: MicroRNAs have recently emerged as a major class of regulatory molecules involved in a broad range of biological processes and complex diseases. Construction of miRNA-target regulatory networks can provide useful information for the study and diagnosis of complex diseases. Many sequence-based and evolutionary information-based methods have been developed to identify miRNA-mRNA targeting relationships. However, as the amount of available miRNA and gene expression data grows, a more statistical and systematic method combining sequence-based binding predictions and expression-based correlation data becomes necessary for the accurate identification of miRNA-mRNA pairs. RESULTS: We propose a Lasso regression model for the identification of miRNA-mRNA targeting relationships that combines sequence-based prediction information, miRNA co-regulation, RISC availability and miRNA/mRNA abundance data. By comparing this modelling approach with two other known methods applied to three different datasets, we found that the Lasso regression model has considerable advantages in both sensitivity and specificity. The regression coefficients in the model can be used to determine the true regulatory efficacies in tissues and was demonstrated using the miRNA target site type data. Finally, by constructing the miRNA regulatory networks in two stages of prostate cancer (PCa), we found the several significant miRNA-hubbed network modules associated with PCa metastasis. In conclusion, the Lasso regression model is a robust and informative tool for constructing the miRNA regulatory networks for diagnosis and treatment of complex diseases. AVAILABILITY: The R program for predicting miRNA-mRNA targeting relationships using the Lasso regression model is freely available, along with the described datasets and resulting regulatory network, at http://biocompute.bmi.ac.cn/CZlab/alarmnet/. The source code is open for modification and application to other miRNA/mRNA expression datasets. CONTACT: zhangcg@bmi.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: MicroRNAs have recently emerged as a major class of regulatory molecules involved in a broad range of biological processes and complex diseases. Construction of miRNA-target regulatory networks can provide useful information for the study and diagnosis of complex diseases. Many sequence-based and evolutionary information-based methods have been developed to identify miRNA-mRNA targeting relationships. However, as the amount of available miRNA and gene expression data grows, a more statistical and systematic method combining sequence-based binding predictions and expression-based correlation data becomes necessary for the accurate identification of miRNA-mRNA pairs. RESULTS: We propose a Lasso regression model for the identification of miRNA-mRNA targeting relationships that combines sequence-based prediction information, miRNA co-regulation, RISC availability and miRNA/mRNA abundance data. By comparing this modelling approach with two other known methods applied to three different datasets, we found that the Lasso regression model has considerable advantages in both sensitivity and specificity. The regression coefficients in the model can be used to determine the true regulatory efficacies in tissues and was demonstrated using the miRNA target site type data. Finally, by constructing the miRNA regulatory networks in two stages of prostate cancer (PCa), we found the several significant miRNA-hubbed network modules associated with PCa metastasis. In conclusion, the Lasso regression model is a robust and informative tool for constructing the miRNA regulatory networks for diagnosis and treatment of complex diseases. AVAILABILITY: The R program for predicting miRNA-mRNA targeting relationships using the Lasso regression model is freely available, along with the described datasets and resulting regulatory network, at http://biocompute.bmi.ac.cn/CZlab/alarmnet/. The source code is open for modification and application to other miRNA/mRNA expression datasets. CONTACT: zhangcg@bmi.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Tianjiao Chu; Jean-Francois Mouillet; Brian L Hood; Thomas P Conrads; Yoel Sadovsky Journal: Bioinformatics Date: 2015-01-24 Impact factor: 6.937
Authors: Sora Yoon; Hai C T Nguyen; Woobeen Jo; Jinhwan Kim; Sang-Mun Chi; Jiyoung Park; Seon-Young Kim; Dougu Nam Journal: Nucleic Acids Res Date: 2019-05-21 Impact factor: 16.971
Authors: Nathanaël Weill; Véronique Lisi; Nicolas Scott; Paul Dallaire; Julie Pelloux; François Major Journal: Nucleic Acids Res Date: 2015-06-18 Impact factor: 16.971
Authors: Steffen Sass; Adriana Pitea; Kristian Unger; Julia Hess; Nikola S Mueller; Fabian J Theis Journal: Int J Mol Sci Date: 2015-12-18 Impact factor: 5.923