scholarly journals A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information

2014 ◽  
Vol 13s7 ◽  
pp. CIN.S16348 ◽  
Author(s):  
Zixing Wang ◽  
Wenlong Xu ◽  
Haifeng Zhu ◽  
Yin Liu
Keyword(s):  
Liver Cancer ◽  
Mrna Expression ◽  
Target Prediction ◽  
Bayesian Framework ◽  
External Information ◽  
Sequence Information ◽  
Expression Data ◽  
Microrna Target ◽  
Mrna Expression Data ◽  
Mirna Targets

MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.

scholarly journals Normalization strategies for mRNA expression data in cartilage research

2008 ◽  
Vol 16 (8) ◽  
pp. 947-955 ◽  
Author(s):  
K. Fundel ◽  
J. Haag ◽  
P.M. Gebhard ◽  
R. Zimmer ◽  
T. Aigner
Keyword(s):  
Mrna Expression ◽  
Expression Data ◽  
Mrna Expression Data

scholarly journals Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

2019 ◽  
Vol 14 (5) ◽  
pp. 432-445 ◽  
Author(s):  
Muniba Faiza ◽  
Khushnuma Tanveer ◽  
Saman Fatihi ◽  
Yonghua Wang ◽  
Khalid Raza
Keyword(s):  
Drosophila Melanogaster ◽  
Mirna Target ◽  
Homo Sapiens ◽  
Target Prediction ◽  
Computational Prediction ◽  
Transcriptional Level ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Prediction Tools ◽  
Mirna Targets

Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets. Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study. Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools. Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives. Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

MiRACLe: an individual-specific approach to improve microRNA-target prediction based on a random contact model

2020 ◽  
Author(s):  
Pan Wang ◽  
Qi Li ◽  
Nan Sun ◽  
Yibo Gao ◽  
Jun S Liu ◽  
...  
Keyword(s):  
Predictive Power ◽  
Expression Profiles ◽  
Target Prediction ◽  
Contact Model ◽  
Prediction Algorithm ◽  
Large Sample Size ◽  
Messenger Rnas ◽  
Expression Data ◽  
Mirna Regulation ◽  
Microrna Target

Abstract Deciphering microRNA (miRNA) targets is important for understanding the function of miRNAs as well as miRNA-based diagnostics and therapeutics. Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA–mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https://github.com/PANWANG2014/miRACLe.

ABCG2 and TOP-1 mRNA expression as predictive biomarkers for adjuvant FOLFIRI treatment in stage III colon cancer patients: Results from the PETAAC-3 prospective randomized clinical trial.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11594-11594
Author(s):  
Nils Brunner ◽  
Jan Stenvang ◽  
Eva Budinska ◽  
Sune Boris Nygaard
Keyword(s):  
Colon Cancer ◽  
Mrna Expression ◽  
Cancer Patients ◽  
Adjuvant Treatment ◽  
Stage Iii ◽  
Expression Data ◽  
P Values ◽  
Mrna Expression Data ◽  
Stage Iii Colon Cancer ◽  
Significant Difference

11594 Background: FOLFIRI as adjuvant treatment in primary colon cancer was previously tested in two pivotal prospective randomized clinical trials (PETACC-3 and CALGB 89803), both of which failed to demonstrate significant beneficial effects when adding irinotecan to 5FU. As a consequence, FOLFIRI is presently not used as adjuvant treatment for colon cancer. Methods: The study included 580 patients with mRNA expression data performed on tumor samples (FFPE) from stage III colon cancer patients enrolled in the PETACC-3 study, which randomized the patients to 5FU plus Leucovorin +/- irinotecan. Primary end-points were recurrence-free survival (RFS) and overall survival (OS). Median ABCG2 and the 75 percentile TOP-1 mRNA expression data were used to allocate the patients into one of two groups: One with high ABCG2 expression (above median) and low TOP-1 expression (below 75 percentile) (n = 167) and another group including all other combinations of these two genes. Kaplan Meier curves and Cox proportional hazards model were used to visualize differences between groups and calculate p-values (log-rank test). Results: The survival statistics showed a significant difference for both RFS (HR: 0.63 (0.44-0.92); p = 0.017) and OS (HR: 0.6 (0.39-0.93); p = 0.021) between the two groups when the patients received FOLFIRI. In contrast, no significant differences were observed between the groups when patients received 5FU and Leucovorin alone (p-values: RFS: 0.58; OS: 0.75). Conclusions: We here show that the combination of two independent gene expression abundance with a strong association to irinotecan treatment (high ABCG2 drug efflux pump and low TOP-1, the latter being the target for irinotecan) identified a group of stage III colon cancer patients who will not benefit from FOLFIRI adjuvant treatment while patients with other combinations of expression of these two genes appear to significantly benefit from adjuvant FOLFIRI treatment. The lack of a similar effect in patients receiving treatment with 5FU and Leucovorin only, points to a predictive value of ABCG2 and TOP-1 measurements.

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