Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks
Protein lysine crotonylation (Kcr) is an important type of post-translational modification that regulates various activities. The experimental approaches to identify the Kcr sites are time-consuming and it is necessary to develop computational prediction approaches. Previously, a few classifiers wer...
Main Authors: | Yiming Zhao, Ningning He, Zhen Chen, Lei Li |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8959202/ |
Similar Items
-
DeepKcrot: A Deep-Learning Architecture for General and Species-Specific Lysine Crotonylation Site Prediction
by: Xilin Wei, et al.
Published: (2021-01-01) -
The Response of Rhodotorula mucilaginosa to Patulin Based on Lysine Crotonylation
by: Qiya Yang, et al.
Published: (2018-09-01) -
Lysine crotonylation is involved in hepatocellular carcinoma progression
by: Junhu Wan, et al.
Published: (2019-03-01) -
Global Proteomic Analysis of Lysine Crotonylation in the Plant Pathogen Botrytis cinerea
by: Ning Zhang, et al.
Published: (2020-10-01) -
Global Lysine Crotonylation Alterations of Host Cell Proteins Caused by Brucella Effector BspF
by: Jinying Zhu, et al.
Published: (2021-01-01)