ArticleComputational and mathematical methods in medicine2021
iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features.Genomics & informatics · 2024Article
- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.Frontiers in medicine · 2023Article
- Computational prediction of promotors inFrontiers in microbiology · 2023Article
- Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting.Frontiers in artificial intelligence · 2023Article
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- Identifying Transcription Factors That Prefer Binding to Methylated DNA Using ReducedACS omega · 2022Article
- iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank.PLoS computational biology · 2022Article
- ATGPred-FL: sequence-based prediction of autophagy proteins with feature representation learning.Amino acids · 2022Article
- Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites inInternational journal of molecular sciences · 2022Article
- i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting.Frontiers in plant science · 2022Article
- A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images.Computational intelligence and neuroscience · 2022Article
- Identification ofComputational and mathematical methods in medicine · 2022Article
- The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning.Frontiers in cell and developmental biology · 2022Article
- iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.Frontiers in microbiology · 2022Article
- Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning.Computational and mathematical methods in medicine · 2022Article
- The Roles of Optogenetics and Technology in Neurobiology: A Review.Frontiers in aging neuroscience · 2022Review
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7 authors.
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Abstract
Bioluminescent proteins (BLPs) are a class of proteins that widely distributed in many living organisms with various mechanisms of light emission including bioluminescence and chemiluminescence from luminous organisms. Bioluminescence has been commonly used in various analytical research methods of cellular processes, such as gene expression analysis, drug discovery, cellular imaging, and toxicity determination. However, the identification of bioluminescent proteins is challenging as they share poor sequence similarities among them. In this paper, we briefly reviewed the development of the computational identification of BLPs and subsequently proposed a novel predicting framework for identifying BLPs based on eXtreme gradient boosting algorithm (XGBoost) and using sequence-derived features. To train the models, we collected BLP data from bacteria, eukaryote, and archaea. Then, for getting more effective prediction models, we examined the performances of different feature extraction methods and their combinations as well as classification algorithms. Finally, based on the optimal model, a novel predictor named iBLP was constructed to identify BLPs. The robustness of iBLP has been proved by experiments on training and independent datasets. Comparison with other published method further demonstrated that the proposed method is powerful and could provide good performance for BLP identification. The webserver and software package for BLP identification are freely available at http://lin-group.cn/server/iBLP.
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