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    Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation

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    Type
    Preprint
    Authors
    Akujuobi, Uchenna Thankgod cc
    Chen, Jun
    Elhoseiny, Mohamed cc
    Spranger, Michael
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-10-05
    Permanent link to this record
    http://hdl.handle.net/10754/665559
    
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    Abstract
    Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation(HG), which refers to the discovery of meaningful implicit connections between biomedical terms. However, most existing methods fail to truly capture the temporal dynamics of scientific term relations and also assume unobserved connections to be irrelevant (i.e., in a positive-negative (PN) learning setting). To break these limits, we formulate this HG problem as future connectivity prediction task on a dynamic attributed graph via positive-unlabeled (PU) learning. Then, the key is to capture the temporal evolution of node pair (term pair) relations from just the positive and unlabeled data. We propose a variational inference model to estimate the positive prior, and incorporate it in the learning of node pair embeddings, which are then used for link prediction. Experiment results on real-world biomedical term relationship datasets and case study analyses on a COVID-19 dataset validate the effectiveness of the proposed model.
    Publisher
    arXiv
    arXiv
    2010.01916
    Additional Links
    https://arxiv.org/pdf/2010.01916
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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