Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

被引:32
作者
Shah, Hayat Ali [1 ]
Liu, Juan [1 ]
Yang, Zhihui [1 ]
Feng, Jing [1 ]
机构
[1] Wuhan Univ, Inst Artificial Intelligence, Sch Comp Sci, Wuhan, Peoples R China
基金
国家重点研发计划;
关键词
machine learning; prediction; metabolic pathway; enzymes; biochemical reaction; substrate; metabolites; MEANS CLUSTERING-ALGORITHM; METACYC DATABASE; SEQUENCE MOTIFS; WEB SERVER; IDENTIFICATION; GENOME; NETWORKS; OPTIMIZATION; ANNOTATION; TOOLS;
D O I
10.3389/fmolb.2021.634141
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.
引用
收藏
页数:11
相关论文
共 92 条
[91]   A Bayesian Approach to Pathway Analysis by Integrating Gene-Gene Functional Directions and Microarray Data [J].
Yifang Zhao ;
Ming-Hui Chen ;
Baikang Pei ;
David Rowe ;
Dong-Guk Shin ;
Wangang Xie ;
Fang Yu ;
Lynn Kuo .
Statistics in Biosciences, 2012, 4 (1) :105-131
[92]   Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property [J].
Zhong, W ;
Altun, G ;
Harrison, R ;
Tai, PC ;
Pan, Y .
IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2005, 4 (03) :255-265