Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences.
Comput Math Methods Med
; 2021: 5529389, 2021.
Article
en En
| MEDLINE
| ID: mdl-34055035
ABSTRACT
Many combinations of protein features are used to improve protein structural class prediction, but the information redundancy is often ignored. In order to select the important features with strong classification ability, we proposed a recursive feature selection with random forest to improve protein structural class prediction. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed feature selection method effectively improves the efficiency of protein structural class prediction. Only less than 5% features are used, but the prediction accuracy is improved by 4.6-13.3%. We further compared different protein features and found that the predicted secondary structural features achieve the best performance. This understanding can be used to design more powerful prediction methods for the protein structural class.
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1
Base de datos:
MEDLINE
Asunto principal:
Proteínas
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Comput Math Methods Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article