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Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. The Dataset was generated by calculating the sequence, sub-sequence, physiochemical, annotation-based features for each 171,212 reviewed proteins using method in. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. It is highly unlikely that all known proteins will ever be completely experimentally characterised. Here the task of experimental characterisation is blind and becomes unwieldy. Orphan proteins without discernible sequence relatives present a greater challenge. For proteins with well characterised close relatives, it is trivial to infer function. Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s). Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. New un-annotated sequences are being added to the public protein databases (e.g. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions.
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Protein function prediction has been the most worked upon and the most challenging problem for computational biologists.