Name
Title | ||
|---|---|---|
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 (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 LSTMWE 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.gpb.2018.08.004 | Genomics, Proteomics & Bioinformatics |
Keywords | Field | DocType |
Deep learning,Recurrent neural network,LSTM,Malonylation,Random forest | Training set,False positive rate,Biology,Protein malonylation,Artificial intelligence,Deep learning,Word embedding,Genetics,Classifier (linguistics),Random forest,Machine learning,Encoding (memory) | Journal |
Volume | Issue | ISSN |
16 | 6 | 1672-0229 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Chen | 1 | 36 | 2.50 |
| Ningning He | 2 | 0 | 0.34 |
| Yu Huang | 3 | 74 | 23.75 |
| Wen Tao Qin | 4 | 0 | 0.34 |
| Xuhan Liu | 5 | 2 | 0.69 |
| Lei Li | 6 | 0 | 0.34 |