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.
Article
in English
| 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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Chemistry
/
Protein Processing, Post-Translational
/
Amino Acid Sequence
/
Machine Learning
/
Forecasting
/
Deep Learning
/
Genetics
/
Amino Acids
/
Lysine
/
Malonates
Type of study:
Prognostic study
Limits:
Animals
Language:
English
Journal:
Genomics, Proteomics & Bioinformatics
Year:
2018
Type:
Article
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