MicroRNA expression prediction: Regression from regulatory elements

• Autorzy: Oğul H. , Tuncer M. E.

Identyfikatory

DOI

10.1016/j.bbe.2015.10.010

• Języki publikacji: EN

Abstrakty

EN

MicroRNAs are known as important actors in post-transcriptional regulation and relevant biological processes. Their expression levels do not only provide information about their own activities but also implicitly explain the behaviors of their targets, thus, in turn, the circuitry of underlying gene regulatory network. In this study, we consider the problem of estimating the expression of a newly discovered microRNA with known promoter sequence in a certain condition where the expression values of some known microRNAs are available. To this end, we offer a regression model to be learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from the regulatory elements found in its promoter sequence. The results obtained through the experiments on real microarray data justify the applicability of the framework in practice.

Słowa kluczowe

EN

MicroRNA; microarray; gene expression

PL

microRNA; mikromacierz; ekspresja genów

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 1
• Strony: 89--94
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr.

Twórcy

autor

Oğul H.

• Department of Computer Engineering, Başkent University, Ankara, Turkey

autor

Tuncer M. E.

• Department of Computer Engineering, Başkent University, Ankara, Turkey

Bibliografia

• [1] Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function,. Cell 2004;116:281–97.
• [2] Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009;136:215–33.
• [3] Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 2009;19:1–11.
• [4] Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature 2005;435:834–8.
• [5] Peng X, Li Y, Walters KA, Rosenzweig ER, Lederer SL, Aicher LD, et al. Computational identification of hepatitis c virus associated microRNA-mRNA regulatory modules in human livers. BMC Genomics 2009;10:373.
• [6] Voorhoeve PM. MicroRNAs: oncogenes, tumor suppressors or master regulators of cancer heterogeneity. Biochim Biophys Acta 2010;1805:72–86.
• [7] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270 (5235):467–70.
• [8] Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics 2001;17:520–5.
• [9] Beer MA, Tavazoie S. Predicting gene expression from sequence. Cell 2004;117(2):185–98.
• [10] Yuan Y, Guo L, Shen L, Liu JS. Predicting gene expression from sequence: a reexamination. PLoS Comput Biol 2007;3 (11):e243.
• [11] Wu C-C, Asgharzadeh S, Triche TJ, D'Argenio DZ. Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning. Bioinformatics 2010;26(6):807–13.
• [12] Li Yh, Lee KK, Walsh S, Smith C, Hadingham S, Sorefan K, et al. Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine. Genome Res 2006;16(3):414–27.
• [13] Tipping ME. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 2001;1:211–44.
• [14] Martins F. In silico analysis of miRNA promoters.[M.Sc. thesis] Universidade de Lisboa; 2011.
• [15] Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30(1):207–10.
• [16] Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acid Res 2006;34(Database issue):D108–10.
• [17] Murphy KP. Machine learning: a probabilistic perspective. Cambridge, MA: The MIT Press; 2012.
• [18] Tipping ME, Faul AC. Fast marginal likelihood maximisation for sparse Bayesian models. In: Bishop CM, Frey BJ, editors. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics. 2003. p. 3–6.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.