Lots of cold-adapted organisms could produce antifreeze proteins (AFPs) to
counter the freezing of cell fluids by controlling the growth of ice crystal. AFPs have been
found in various species such as in vertebrates, invertebrates, plants, bacteria, and fungi.
These AFPs from fish, insects and plants displayed a high diversity. Thus, the
identification of the AFPs is a challenging task in computational proteomics. With the
accumulation of AFPs and development of machine meaning methods, it is possible to
construct a high-throughput tool to timely identify the AFPs. In this review, we briefly
reviewed the application of machine learning methods in antifreeze proteins identification
from difference section, including published benchmark dataset, sequence descriptor,
classification algorithms and published methods. We hope that this review will produce
new ideas and directions for the researches in identifying antifreeze proteins.
Keywords: Antifreeze protein, classification, machine learning, computational proteomics, cold-adapted organisms, cell fluids.
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