BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection
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ArticleAuthors
Kandaswamy, Krishna KumarGanesan, Pugalenthi
Hazrati, Mehrnaz Khodam
Kalies, Kai-Uwe
Martinetz, Thomas
KAUST Department
Bioscience Core LabStructural and Functional Bioinformatics Group
Date
2011-08-17Online Publication Date
2011-08-17Print Publication Date
2011Permanent link to this record
http://hdl.handle.net/10754/325467
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Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.Citation
Kandaswamy K, Pugalenthi G, Hazrati M, Kalies K-U, Martinetz T (2011) BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection. BMC Bioinformatics 12: 345. doi:10.1186/1471-2105-12-345.Publisher
Springer NatureJournal
BMC BioinformaticsPubMed ID
21849049PubMed Central ID
PMC3176267ae974a485f413a2113503eed53cd6c53
10.1186/1471-2105-12-345
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