Prediction of diabetic protein markers based on an ensemble method

Qu, K. Y.; Zou, Quan; Shi, Hua

doi:10.52586/4935

Cited by 6 publications

(2 citation statements)

References 70 publications

(105 reference statements)

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“…SVMProt-188D encodes peptide sequences based on the frequency of 20 different amino acid compositions of the peptide sequence and eight physical and chemical characteristics. SVMProt-188D has effectively predicted diabetic protein markers ( Qu et al, 2021 ) and antioxidant proteins ( Caa et al, 2020 ). AAIndex encodes peptide sequences based on the AAIndex database, which consists of many physical and chemical properties of amino acids.…”

Section: Methodsmentioning

confidence: 99%

ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

Chen

et al. 2023

Front. Genet.

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Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.

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Section: Methodsmentioning

confidence: 99%

ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

Chen

et al. 2023

Front. Genet.

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“…With the rapid development of society, people have higher and higher requirements for medical and health care ( Lin, 2020 ). Therefore, it is urgent to learn more about the structure and function of proteins in order to explain more of the meaning of life and promote the development of biomedicine and other fields ( Wang et al, 2020a ; Qu et al, 2021 ). However, there is a difficulty in the current research, that is, how to use its sequence information to predict proteins effectively.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

Guo

Hou²,

Zhu

et al. 2021

Front. Genet.

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Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body’s life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.

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Chronic diseases monitoring and diagnosis system based on features selection and machine learning predictive models

et al. 2022

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Prediction of diabetic protein markers based on an ensemble method

Cited by 6 publications

References 70 publications

ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

Chronic diseases monitoring and diagnosis system based on features selection and machine learning predictive models

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