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What is deepNEC-2.0?

This is an alignment-free computational approach to predicted nitrogen biochemical network-related enzymes from the sequence itself. deepNEC-2.0 is a novel end-to-end feature selection and classification model training approach for nitrogen biochemical network-related enzyme prediction. The algorithm was developed using Deep Learning, a class of machine learning algorithms that uses multiple layers to extract higher-level features from the raw input data. The derived protein sequence is used as an input, extracting sequential and convolutional features from raw encoded protein sequences based on classification rather than traditional alignment-based methods for enzyme prediction.