Authors:
Bishnu Sarker
;
David W. Ritchie
and
Sabeur Aridhi
Affiliation:
University of Lorraine, Inria, Loria, CNRS, F-54000, Nancy and France
Keyword(s):
Machine Learning, Representation Learning, Protein Function Annotation, Bioinformatics, Domain Embedding.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Due to the recent advancement in genomic sequencing technologies, the number of protein sequences in public databases is growing exponentially. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. The May 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 158 million protein sequences. For the complete exploitation of this huge knowledge base, protein sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. However, there is only about half a million sequences (UniprotKB/SwissProt) are reviewed and functionally annotated by expert curators using information extracted from the published literature and computational analyses. The manual annotation by experts are expensive, slow and insufficient to fill the gap between the annotated and unannotated protein sequences. In this paper, we present an automatic functional anno
tation technique using neural network based based word embedding exploiting domain and family information of proteins. Domains are the most conserved regions in protein sequences and constitute the building blocks of 3D protein structures. To do the experiment, we used fastText1, a library for learning of word embeddings and text classification developed by Facebook’s AI Research lab. The experimental results show that domain embeddings perform much better than k-mer based word embeddings.
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