On the Computational Prediction of miRNA Promoters - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

On the Computational Prediction of miRNA Promoters

Charalampos Michail
  • Fonction : Auteur
  • PersonId : 1012003
Aigli Korfiati
  • Fonction : Auteur
  • PersonId : 991089
Konstantinos Theofilatos
  • Fonction : Auteur
  • PersonId : 1012004
Spiros Likothanassis
  • Fonction : Auteur
  • PersonId : 992360

Résumé

MicroRNAs transcription regulation is an open topic in molecular biology and the identification of the promoters of microRNAs would give us relevant insights on cellular regulatory mechanisms. In the present study, we introduce a new computational methodology for the prediction of microRNA promoters, which is based on the hybrid combination of an adaptive genetic algorithm with a nu-Support Vector Regression (nu-SVR) classifier. This methodology uses genetic algorithms to locate the optimal features set and to optimize the parameters of the nu-SVR classifier. The main advantage of the proposed solution is that it systematically studies and calculates a vast number of features that can be used for promoters prediction including frequency-based properties, regulatory elements and epigenetic features. The proposed method also handles efficiently the issues of over-fitting, feature selection, convergence and class imbalance. Experimental results give accuracy over 87 % in the miRNA promoter prediction.
Fichier principal
Vignette du fichier
430537_1_En_51_Chapter.pdf (254.07 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01557623 , version 1 (06-07-2017)

Licence

Paternité

Identifiants

Citer

Charalampos Michail, Aigli Korfiati, Konstantinos Theofilatos, Spiros Likothanassis, Seferina Mavroudi. On the Computational Prediction of miRNA Promoters. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.573-583, ⟨10.1007/978-3-319-44944-9_51⟩. ⟨hal-01557623⟩
168 Consultations
119 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More