Sarker develops Prot-A-GAN method for automatic protein function annotation
As of December 2021, there are more than 220 million publicly available protein sequences stored in the UniProtKB protein database awaiting manual annotation by expert biocurators
Sarker develops Prot-A-GAN method for automatic protein function annotation
As of December 2021, there are more than 220 million publicly available protein sequences stored in the UniProtKB protein database awaiting manual annotation by expert biocurators. That work will uncover the links between proteins and their functions in living organisms. It can improve understanding of diseases and advance drug discovery. But, because the volume of this data is so massive, those annotations are nearly impossible to do manually.
“It is essential to use artificial intelligence to develop accurate, automatic protein function annotation techniques,” says Bishnu Sarker, Ph.D., assistant professor, computer science and data science.
Some of those techniques have been developed, but Dr. Sarker explored a new path by learning numerical representation of proteins from a biomedical knowledge graph leveraging the power of generative adversarial network (GAN).
Dr. Sarker and his colleagues Marie-Dominique Devignes, associate researcher, CNRS; Guy Wolf, associate professor, Université de Montréal; and Sabeur Aridhi, associate professor in computer science, University of Lorraine, published their study as Prot-A-GAN: Automatic Protein Function Annotation using GAN-inspired Knowledge Graph Embedding.
Dr. Sarker and his partners used a knowledge graph to connect diverse sources of biological data relating to proteins to identify positive links, known as triples, between proteins and Gene Ontology (GO) annotations that describe the function of a particular protein. Then, they trained a complex machine learning model putting together the strengths of GAN, random-walk, and reinforcement learning. In Prot-A-GAN, all these ingredients are put toge