Name
Title | ||
|---|---|---|
SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
Motivation: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. Results: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa156 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 11 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manaz Kaleel | 1 | 0 | 0.68 |
| Yandan Zheng | 2 | 0 | 0.34 |
| Jialiang Chen | 3 | 0 | 0.34 |
| Xuanming Feng | 4 | 0 | 0.34 |
| Jeremy C. Simpson | 5 | 28 | 3.76 |
| Gianluca Pollastri | 6 | 736 | 62.68 |
| Catherine Mooney | 7 | 0 | 1.35 |