Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role
of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting
the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized
to be essential for many medical, commercial and other general technological advances. Therefore, the
prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence
and promote the development of application of bioluminescence. Since the experimental methods are money
and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and
accurate prediction of BLPs by combining their sequences information with machine learning methods. In this
review, we summarized and compared the application of machine learning methods in the prediction of BLPs
from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.
Keywords: Bioluminescent proteins, machine learning methods, sequence-derived features, feature analysis, bioinformatics tools.
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