Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.

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_WE) for the prediction of mammalian malonylation sites. LSTM_WE 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_WE is sensitive to the size of the training set, but this limitation can be overcome by integration with a trad

SUBMITTER: Chen Z 

PROVIDER: S-EPMC6411950 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature