ArticleInternational journal of molecular sciences2012
Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles.
Article in International journal of molecular sciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants.The plant genome · 2024Article
- iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.Computational and mathematical methods in medicine · 2021Article
- Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine.Evolutionary bioinformatics online · 2019Article
- Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme.BMC bioinformatics · 2017Article
- Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.BMC bioinformatics · 2016Article
- Propensity scores for prediction and characterization of bioluminescent proteins from sequences.PloS one · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins' functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.
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Registered trials
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