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    Gene Ontology Capsule GAN: an improved architecture for protein function prediction

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    Author
    Mansoor, Musadaq
    Nauman, Mohammad
    Rehman, Hafeez Ur
    Omar, Maryam
    Subject
    Research Subject Categories::TECHNOLOGY::Information technology::Computer science::Computer science
    Date
    2022-08-15
    
    Metadata
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    Abstract
    Proteins are the core of all functions pertaining to living things. They consist of an extended amino acid chain folding into a three-dimensional shape that dictates their behavior. Currently, convolutional neural networks (CNNs) have been pivotal in predicting protein functions based on protein sequences. While it is a technology crucial to the niche, the computation cost and translational invariance associated with CNN make it impossible to detect spatial hierarchies between complex and simpler objects. Therefore, this research utilizes capsule networks to capture spatial information as opposed to CNNs. Since capsule networks focus on hierarchical links, they have a lot of potential for solving structural biology challenges. In comparison to the standard CNNs, our results exhibit an improvement in accuracy. Gene Ontology Capsule GAN (GOCAPGAN) achieved an F1 score of 82.6%, a precision score of 90.4% and recall score of 76.1%.
    Department
    Computer Science
    Publisher
    PeerJ
    Journal title
    PeerJ Computer Science
    DOI
    https://doi.org/10.7717%2Fpeerj-cs.1014
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.7717%2Fpeerj-cs.1014
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