DaDL-SChlo: protein subchloroplast localization prediction based on generative adversarial networks and pre-trained protein language model.
Brief Bioinform
; 24(3)2023 05 19.
Article
en En
| MEDLINE
| ID: mdl-36929854
ABSTRACT
Chloroplast is a crucial site for photosynthesis in plants. Determining the location and distribution of proteins in subchloroplasts is significant for studying the energy conversion of chloroplasts and regulating the utilization of light energy in crop production. However, the prediction accuracy of the currently developed protein subcellular site predictors is still limited due to the complex protein sequence features and the scarcity of labeled samples. We propose DaDL-SChlo, a multi-location protein subchloroplast localization predictor, which addresses the above problems by fusing pre-trained protein language model deep learning features with traditional handcrafted features and using generative adversarial networks for data augmentation. The experimental results of cross-validation and independent testing show that DaDL-SChlo has greatly improved the prediction performance of protein subchloroplast compared with the state-of-the-art predictors. Specifically, the overall actual accuracy outperforms the state-of-the-art predictors by 10.7% on 10-fold cross-validation and 12.6% on independent testing. DaDL-SChlo is a promising and efficient predictor for protein subchloroplast localization. The datasets and codes of DaDL-SChlo are available at https//github.com/xwanggroup/DaDL-SChlo.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Cloroplastos
/
Lenguaje
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
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INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article
País de afiliación:
China