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.
CITATION STYLE
Al Bkhetan, Z., & Plewczynski, D. (2017). Multi-levels 3D chromatin interactions prediction using epigenomic profiles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10352 LNAI, pp. 19–28). Springer Verlag. https://doi.org/10.1007/978-3-319-60438-1_2
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