Journal
GENOMICS PROTEOMICS & BIOINFORMATICS
Volume 16, Issue 6, Pages 451-459Publisher
ELSEVIER
DOI: 10.1016/j.gpb.2018.08.004
Keywords
Deep learning; Recurrent neural network; LSTM; Malonylation; Random forest
Categories
Funding
- Young Scientists Fund of the National Natural Science Foundation of China [31701142, 81602621]
- Qingdao Postdoctoral Science Foundation [2016061]
- Shandong Provincial Natural Science Foundation [ZR2016CM14]
- National Natural Science Foundation of China [31770821]
- Distinguished Expert of Overseas Tai Shan Scholarprogram
Ask authors/readers for more resources
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 (LSTMWE) for the prediction of mammalian malonylation sites. LSTMWE 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 traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTMWE 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available