Figure 1. The EFFECT framework consists of two algorithms, EFC and EFS, as detailed in the Methods section.

While EFC conducts a biased exploration of a vast space of potentially complex features to find a set of top features, EFS reduces this set to a subset of informative yet low redundancy features. The remaining features are used to transform sequence data into vector data that can be separated by any classifier.