Fig. 3.5 is a schematic representation of a hierarchical classifier where the classifier 1 and classifier 2 can be either a machine learning based or a deep learning based classifier. Fig. 3.5. Of all the types, the universal classification ismon. Picking one class over another is mostly a cosmetic or side band piece of information.

Protein classification represents an ideal test bed for NLP methods. Most importantly, a single, universal model architecture with no task specific modifications apart from a fine tuning step that operates on the sequence of amino acids alone is able to reach or even exceed state of the art performance on a number of protein classification tasks.

Several machine learning classifiers have been developed and is available in the literature for each of the classification types. But the major limitation of all the classifiers in the literature is that, the classifiers are limited only to the particular type of classification problem for which it has been trained.

What is claimed is: 1.puter implementedprising: receiving an untrained hierarchical classifier than includes an artificialwork that includes a directed graph model including a plurality of nodes and a plurality of directed connections among and between the nodes and training the untrained hierarchical classifier algorithm to obtain a trained