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Table 2 Computational methods for target gene prediction

From: A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods

Method

Description

Reference

Unsupervised methods

 Distance

Ranks pairs by inverse linear distance

 

 DNase-DNase

Calculates the Pearson correlation coefficient between the DNase signals at enhancers and promoters across 32 cell-type categories.

[22]

 DNase-expression

Calculates the Pearson correlation coefficient between the normalized DNase signals at enhancers and normalized gene expression levels measured by microarray across 112 cell types.

[23]

 GeneHancer

Cell-type agnostic predictions based on co-expression correlations, CHi-C interactions, eQTLs, and genomic distance

[31]

 Average-rank

Combines the distance and DNase-expression methods by averaging the rank of for each prediction between the two methods

 

Supervised methods

 PEP-motif

Features: frequency of motif instances at enhancers and promoters

[28]

Classifier: Gradient boosting (XGB package)

 TargetFinder

Features: Cell-type-specific epigenomic signals (ChIP-seq, DNase, CAGE, etc.) at enhancers, promoters, and the intervening window between enhancers and promoters.

[27]

Classifier: Gradient boosting (scikit learn)