DeepRCI: Predicting ATP-Binding Proteins Using the Residue-Residue Contact Information

卷积神经网络 计算机科学 超参数 残留物(化学) 三磷酸腺苷 结合位点 人工智能 数据挖掘 化学 模式识别(心理学) 生物系统 生物化学 生物
作者
Zhaoxi Zhang,Yulan Zhao,Juan Wang,Maozu Guo
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:26 (6): 2822-2829 被引量:2
标识
DOI:10.1109/jbhi.2021.3137840
摘要

Adenine-5'-triphosphate (ATP) is a direct energy source for various activities of tissues and cells in the body. The release of ATP energies requires the assistance of ATP-binding proteins. Therefore, the identification of ATP-binding proteins is of great significance for the research on organisms. So far, there are several methods for predicting ATP-binding proteins. However, the accuracies of these methods are so low that the predicted proteins are inaccurate. Here, we designed a novel method, called as DeepRCI (based on Deep convolutional neural network and Residue-residue Contact Information), for predicting ATP-binding proteins. In order to maximize the performance of our method, we experimented with different hyperparameters and finally chose a 12-depth-512-filters deep convolutional neural network with an input size of 448*448. By using this model, DeepRCI achieved an accuracy of 93.61% on the test set which means a significant improvement of 11.78% over the state-of-the-art methods. We also compared the performance of residue-residue contact information datasets with different noise levels which are mainly due to gaps in the multiple sequence alignment. Compared with the low-noise dataset, the prediction accuracy on the high-noise dataset is reduced by 6.78%, which affects the performance of DeepRCI to a certain extent. We believe that with the increase of sequence data, this problem will eventually be solved. Finally, we provide a web service of DeepRCI which link can be obtained in Data Availability.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Lucas应助HHHAN采纳,获得10
2秒前
无情修杰完成签到 ,获得积分10
3秒前
小柒完成签到 ,获得积分10
5秒前
聪慧芷巧发布了新的文献求助10
6秒前
7秒前
11秒前
蓝意完成签到,获得积分0
12秒前
xiaohongmao完成签到,获得积分10
17秒前
20秒前
qweerrtt完成签到,获得积分10
27秒前
27秒前
与共发布了新的文献求助10
28秒前
carly完成签到 ,获得积分10
29秒前
颢懿完成签到 ,获得积分10
32秒前
量子星尘发布了新的文献求助10
33秒前
35秒前
ljc完成签到 ,获得积分10
36秒前
Java完成签到,获得积分10
40秒前
42秒前
鲤鱼安青完成签到 ,获得积分10
44秒前
44秒前
dollarpuff完成签到 ,获得积分10
47秒前
47秒前
mmmmmMM完成签到,获得积分10
54秒前
luckweb完成签到,获得积分10
1分钟前
猫的毛完成签到 ,获得积分10
1分钟前
nicky完成签到 ,获得积分10
1分钟前
麦子完成签到 ,获得积分10
1分钟前
1分钟前
Wilson完成签到 ,获得积分10
1分钟前
luckweb发布了新的文献求助10
1分钟前
1分钟前
1分钟前
1分钟前
传奇3应助wujiwuhui采纳,获得10
1分钟前
开心寄松完成签到,获得积分10
1分钟前
北宫完成签到 ,获得积分10
1分钟前
wansida完成签到,获得积分10
1分钟前
QXS完成签到 ,获得积分10
1分钟前
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038066
求助须知:如何正确求助?哪些是违规求助? 3575779
关于积分的说明 11373801
捐赠科研通 3305584
什么是DOI,文献DOI怎么找? 1819239
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022