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Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy.
- Source :
- PLoS Computational Biology; 12/18/2018, Vol. 14 Issue 12, p1-7, 7p, 3 Graphs
- Publication Year :
- 2018
-
Abstract
- We report an experimental design issue in recent machine learning formulations of the enhancer-promoter interaction problem arising from the fact that many enhancer-promoter pairs share features. Cross-fold validation schemes which do not correctly separate these feature sharing enhancer-promoter pairs into one test set report high accuracy, which is actually arising from high training set accuracy and a failure to properly evaluate generalization performance. Cross-fold validation schemes which properly segregate pairs with shared features show markedly reduced ability to predict enhancer-promoter interactions from epigenomic state. Parameter scans with multiple models indicate that local epigenomic features of individual pairs of enhancers and promoters cannot distinguish those pairs that interact from those which do with high accuracy, suggesting that additional information is required to predict enhancer-promoter interactions. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
ACCURACY
TRAINING
TECHNOLOGICAL innovations
GENERALIZATION
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 14
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 133613101
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1006625