DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural networks based on molecular properties

代谢途径 计算机科学 人工神经网络 人工智能 关系(数据库) 深度学习 代谢网络 计算生物学 图形 理论计算机科学 化学 数据挖掘 生物 生物化学 基因
作者
Hayat Ali Shah,Juan Liu,Zhengwei Yang,Yalan Yan,Qiang Zhang,Jing Feng
出处
期刊:Journal of Bioinformatics and Computational Biology [World Scientific]
卷期号:21 (04) 被引量:1
标识
DOI:10.1142/s0219720023500178
摘要

Metabolic pathways play a crucial role in understanding the biochemistry of organisms. In metabolic pathways, modules refer to clusters of interconnected reactions or sub-networks representing specific functional units or biological processes within the overall pathway. In pathway modules, compounds are major elements and refer to the various molecules that participate in the biochemical reactions within the pathway modules. These molecules can include substrates, intermediates and final products. Determining the presence relation of compounds and pathway modules is essential for synthesizing new molecules and predicting hidden reactions. To date, several computational methods have been proposed to address this problem. However, all methods only predict the metabolic pathways and their types, not the pathway modules. To address this issue, we proposed a novel deep learning model, DeepRT that integrates message passing neural networks (MPNNs) and transformer encoder. This combination allows DeepRT to effectively extract global and local structure information from the molecular graph. The model is designed to perform two tasks: first, determining the present relation of the compound with the pathway module, and second, predicting the relation of query compound and module classes. The proposed DeepRT model evaluated on a dataset comprising compounds and pathway modules, and it outperforms existing approaches.
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
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
科研通AI5应助勤劳影子采纳,获得10
刚刚
栗子发布了新的文献求助10
刚刚
1秒前
XHT驳回了LL应助
2秒前
2秒前
2秒前
hyp发布了新的文献求助10
2秒前
3秒前
4秒前
微笑香薇发布了新的文献求助10
4秒前
777完成签到,获得积分10
4秒前
5秒前
5秒前
黄丽军发布了新的文献求助10
5秒前
5秒前
5秒前
6秒前
小彻完成签到,获得积分10
6秒前
慕青应助研友_nxGqeL采纳,获得20
6秒前
6秒前
7秒前
shang发布了新的文献求助10
7秒前
Giroro_roro完成签到,获得积分10
7秒前
8秒前
完美世界应助橘子采纳,获得10
8秒前
三口发布了新的文献求助10
8秒前
CipherSage应助lisali采纳,获得10
9秒前
夏天来了发布了新的文献求助30
9秒前
dmj发布了新的文献求助10
9秒前
完美世界应助绿夏采纳,获得30
9秒前
dong发布了新的文献求助10
10秒前
ssssssssci完成签到,获得积分10
10秒前
10秒前
11秒前
牛牛发布了新的文献求助10
11秒前
思源应助初晴采纳,获得10
11秒前
景木游发布了新的文献求助10
12秒前
roy_chiang发布了新的文献求助10
13秒前
勤劳影子发布了新的文献求助10
13秒前
英俊的铭应助难得糊涂zq采纳,获得10
13秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 710
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Virulence Mechanisms of Plant-Pathogenic Bacteria 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3564116
求助须知:如何正确求助?哪些是违规求助? 3137325
关于积分的说明 9421827
捐赠科研通 2837701
什么是DOI,文献DOI怎么找? 1559976
邀请新用户注册赠送积分活动 729224
科研通“疑难数据库(出版商)”最低求助积分说明 717246