| Literature DB >> 31696804 |
Dan Zhang1, Zheng-Xing Guan1, Zi-Mei Zhang1, Shi-Hao Li1, Fu-Ying Dao1, Hua Tang2, Hao Lin1.
Abstract
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.Keywords: Bioluminescent proteins; bioinformatics tools; feature analysis; machine learning methods; sequence-derived features.
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Year: 2019 PMID: 31696804 DOI: 10.2174/1381612825666191107100758
Source DB: PubMed Journal: Curr Pharm Des ISSN: 1381-6128 Impact factor: 3.116