Name
Abstract | ||
|---|---|---|
S-succinylation of proteins is a significant and common post-translational modification (PTM) that takes place on Cysteine. And in many biological processes, PTM plays an important role, which is also closely related to many diseases in humans. Hence, identifying the s-succinylation sites of Cysteine is very pivotal in biology and disease research. However, traditional experimental methods are expensive and time-consuming, so ML methods have been proposed by some researchers to deal with the problem of PTM recognition. In particular, the deep learning method is also applied to this field. We put forward a convolutional neural network to identify the hidden sites of s-succinylation in our work. In addition, we utilized the datasets of human and mouse, and we aim to predict the s-succ sites existing in humans, and verify them by loo verification method. More specifically, five metrics are utilized to assess the prediction performance of classifier. In general, CNN model that we proposed achieves better prediction performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-13829-4_62 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2022, PT II |
Keywords | DocType | Volume |
Cysteine succinylation, Convolutional neural network, Machine learning, Protein post-translational modification | Conference | 13394 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tong Meng | 1 | 0 | 0.34 |
| Yuehui Chen | 2 | 0 | 1.01 |
| Baitong Chen | 3 | 0 | 1.35 |
| Yi Cao | 4 | 0 | 1.01 |
| Jiazi Chen | 5 | 0 | 1.35 |
| Hanhan Cong | 6 | 0 | 1.35 |