Multi-levels 3D chromatin interactions prediction using epigenomic profiles
- Ziad Al Bkhetan,
- Dariusz Plewczyński
Identification of the higher-order genome organization has become a critical issue for better understanding of how one dimensional genomic information is being translated into biological functions. In this study, we present a supervised approach based on Random Forest classifier to predict genome-wide three-dimensional chromatin interactions in human cell lines using 1D epigenomics profiles. At the first level of our in silico procedure we build a large collection of machine learning predictors, each one targets single topologically associating domain (TAD). The results are collected and genome-wide prediction is performed at the second level of multi-scale statistical learning model. Initial tests show promising results confirming the previously reported studies. Results were compared with Hi-C and ChIA-PET experimental data to evaluate the quality of the predictors. The system achieved 0.9 for the area under ROC curve, and 0.86–0.89 for accuracy, sensitivity and specificity.
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- Kryszkiewicz Marzena, Marzena Kryszkiewicz Appice Annalisa , Annalisa Appice Ślęzak Dominik Dominik Ślęzak [et al.] (eds.): Foundations of Intelligent Systems.The 23rd International Symposium on Foundations of Intelligent Systems, ISMIS 2017, Proceedings, Lecture Notes in Artificial Intelligence, vol. 10352, 2017, Springer, Cham, Springer, 747 p., ISBN 978-3-319-60437-4. DOI:10.1007/978-3-319-60438-1 Opening in a new tab
- DOI:10.1007/978-3-319-60438-1_2 Opening in a new tab
- (en) English
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- = 20.0, 10-06-2021, ChapterFromConference
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- = 0; = 0
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