TY - GEN
T1 - Multi-levels 3D chromatin interactions prediction using epigenomic profiles
AU - Al Bkhetan, Ziad
AU - Plewczynski, Dariusz
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - 3D chromatin interactions
KW - 3D genome organization
KW - CTCF motifs
KW - ChIA-PET
KW - Chromatin contact domains
KW - Chromatin looping
KW - Epigenomics
KW - Hi-C
KW - Physical interactions
KW - Random Forest
KW - Statistical learning
KW - Topologically associated domains
UR - http://www.scopus.com/inward/record.url?scp=85021871918&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-60438-1_2
DO - 10.1007/978-3-319-60438-1_2
M3 - Conference contribution
AN - SCOPUS:85021871918
SN - 9783319604374
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 28
BT - Foundations of Intelligent Systems - 23rd International Symposium, ISMIS 2017, Proceedings
A2 - Kryszkiewicz, Marzena
A2 - Rybinski, Henryk
A2 - Ras, Zbigniew W.
A2 - Slezak, Dominik
A2 - Skowron, Andrzej
A2 - Appice, Annalisa
A2 - Skowron, Andrzej
PB - Springer Verlag
T2 - 23rd International Symposium on Methodologies for Intelligent Systems, ISMIS 2017
Y2 - 26 June 2017 through 29 June 2017
ER -