Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites / 基因组蛋白质组与生物信息学报·英文版
Genomics, Proteomics & Bioinformatics
;
(4): 451-459, 2018.
Artículo
en Inglés
| WPRIM
| ID: wpr-772962
ABSTRACT
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTM) for the prediction of mammalian malonylation sites. LSTM performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTM is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTM and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http//www.bioinfogo.org/lemp.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Química
/
Procesamiento Proteico-Postraduccional
/
Secuencia de Aminoácidos
/
Aprendizaje Automático
/
Predicción
/
Aprendizaje Profundo
/
Genética
/
Aminoácidos
/
Lisina
/
Malonatos
Tipo de estudio:
Estudio pronóstico
Límite:
Animales
Idioma:
Inglés
Revista:
Genomics, Proteomics & Bioinformatics
Año:
2018
Tipo del documento:
Artículo
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