we proposed an integrative machine-learning-based framework called Enhancer-IF for identifying cell-specific enhancers. Enhancer-IF comprehensively explores a wide range of heterogeneous features with five commonly used machine learning methods (random forest, extremely randomized tree, multilayer perceptron, support vector machine, and extreme gradient boosting). Specifically, these five classifiers were trained with seven encodings and obtained 35 baseline models. The output of these baseline models is integrated and again inputted to five classifiers to construct five meta-models. Finally, the integration of five meta-models through ensemble learning improves the model robustness.
