Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Meta-reg: A Computational Metaheuristic Framework to Improve SVM-Based Prediction of Regulatory Activity
verfasst von : Dong Do Duc, Huan Hoang Xuan, Huy Q. Dinh
Erschienen in: 4th International Conference on Biomedical Engineering in Vietnam
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Gene regulatory activity prediction is an important step to understand which Transcription Factors (TFs) are important for regulation of gene expression in cells. The development of recent high throughput technologies and machine learning approaches allow us to archive this task more efficiently. Support Vector Machine (SVM) has been successfully applied for the case of predicting gene regulatory activity in
Drosophila
embryonic development. Here, we introduce meta-heuristic approaches to select the best parameters for regulatory prediction from transcription factor binding profiles. Experimental results show that our approach outperforms existing methods and the potentials for further analysis beyond the prediction.