Predicting the Types of Metabolic Pathway of Compounds Using Molecular Fragments and Sequential Minimal Optimization

被引:47
作者
Chen, Lei [1 ]
Chu, Chen [2 ]
Feng, Kaiyan [3 ]
机构
[1] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China
[2] Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Biochem & Cell Biol, Shanghai 200031, Peoples R China
[3] Guangdong AIB Polytech Coll, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Compound; metabolic pathway; molecular fragment; minimum redundancy maximum relevance; incremental feature selection; sequential minimal optimization; FUNCTIONAL-GROUP; IDENTIFICATION; INFORMATION; METHYLATION; ALGORITHM; NETWORKS; STITCH;
D O I
10.2174/1386207319666151110122453
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
A metabolic pathway is a series of biological processes providing necessary molecules and energies for an organism, which could be essential to the lives of the living organisms. Most metabolic pathways require the involvement of compounds and given a compound it is helpful to know what types of metabolic pathways the compound participates in. In this study, compounds are first represented by molecular fragments which are then delivered to a prediction engine called Sequential Minimal Optimization (SMO) for predictions. Maximum relevance and minimum redundancy (mRMR) and incremental feature selection are adopted to extract key features based on which an optimal prediction engine is established. The proposed method is effective comparing to the random forest, Dagging and a popular method that integrating chemical-chemical interactions and chemical-chemical similarities. We also make predictions using some compounds with unknown metabolic pathways and choose 17 compounds for analysis. The results indicate that the method proposed may become a useful tool in predicting and analyzing metabolic pathways.
引用
收藏
页码:136 / 143
页数:8
相关论文
共 42 条
[1]   Glucocorticoid Paradoxically Recruits Adipose Progenitors and Impairs Lipid Homeostasis and Glucose Transport in Mature Adipocytes [J].
Ayala-Sumuano, Jorge-Tonatiuh ;
Velez-delValle, Cristina ;
Beltran-Langarica, Alicia ;
Marsch-Moreno, Meytha ;
Hernandez-Mosqueira, Claudia ;
Kuri-Harcuch, Walid .
SCIENTIFIC REPORTS, 2013, 3
[2]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[3]   Prediction of compounds' biological function (metabolic pathways) based on functional group composition [J].
Cai, Yu-Dong ;
Qian, Ziliang ;
Lu, Lin ;
Feng, Kai-Yan ;
Meng, Xin ;
Niu, Bing ;
Zhao, Guo-Dong ;
Lu, Wen-Cong .
MOLECULAR DIVERSITY, 2008, 12 (02) :131-137
[4]   Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models [J].
Chen, Lei ;
Chu, Chen ;
Huang, Tao ;
Kong, Xiangyin ;
Cai, Yu-Dong .
AMINO ACIDS, 2015, 47 (07) :1485-1493
[5]   A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes [J].
Chen, Lei ;
Lu, Jing ;
Zhang, Ning ;
Huang, Tao ;
Cai, Yu-Dong .
MOLECULAR BIOSYSTEMS, 2014, 10 (04) :868-877
[6]   Prediction of Metabolic Pathway Using Graph Property, Chemical Functional Group and Chemical Structural Set [J].
Chen, Lei ;
Zeng, Wei-Ming ;
Cai, Yu-Dong ;
Huang, Tao .
CURRENT BIOINFORMATICS, 2013, 8 (02) :200-207
[7]   Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities [J].
Chen, Lei ;
Zeng, Wei-Ming ;
Cai, Yu-Dong ;
Feng, Kai-Yan ;
Chou, Kuo-Chen .
PLOS ONE, 2012, 7 (04)
[8]   Identifying Protein Complexes Using Hybrid Properties [J].
Chen, Lei ;
Shi, Xiaohe ;
Kong, Xiangyin ;
Zeng, Zhenbing ;
Cai, Yu-Dong .
JOURNAL OF PROTEOME RESEARCH, 2009, 8 (11) :5212-5218
[9]  
Du PF, 2011, EXPERT REV PROTEOMIC, V8, P391, DOI [10.1586/EPR.11.20, 10.1586/epr.11.20]
[10]   Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis [J].
Frades, Itziar ;
Resjo, Svante ;
Andreasson, Erik .
BMC BIOINFORMATICS, 2015, 16