Literature DB >> 19689424

Prediction of small molecules' metabolic pathways based on functional group composition.

Jin Lu1, Bing Niu, Liang Liu, Wen-Cong Lu, Yu-Dong Cai.   

Abstract

How to correctly and efficiently determine small molecules' biological function is a challenge and has a positive effect on further metabonomics analysis. Here, we introduce a computational approach to address this problem. The new approach is based on AdaBoost method and featured by function group composition to the metabolic pathway analysis, which can fast and automatically map the small chemical molecules back to the possible metabolic pathway that they belong to. As a result, jackknife cross validation test and independent set test on the model reached 73.7% and 73.8%, respectively. It can be concluded that the current approach is very promising for mapping some unknown molecules' possible metabolic pathway. An online predictor developed by this research is available at http://chemdata.shu.edu.cn/pathway.

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Year:  2009        PMID: 19689424     DOI: 10.2174/092986609788923374

Source DB:  PubMed          Journal:  Protein Pept Lett        ISSN: 0929-8665            Impact factor:   1.890


  5 in total

1.  Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.

Authors:  Yu-Fei Gao; Lei Chen; Yu-Dong Cai; Kai-Yan Feng; Tao Huang; Yang Jiang
Journal:  PLoS One       Date:  2012-09-21       Impact factor: 3.240

2.  Predicting biological functions of compounds based on chemical-chemical interactions.

Authors:  Le-Le Hu; Chen Chen; Tao Huang; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-12-29       Impact factor: 3.240

3.  iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network.

Authors:  Yuanyuan Zhu; Bin Hu; Lei Chen; Qi Dai
Journal:  Comput Math Methods Med       Date:  2021-01-04       Impact factor: 2.238

Review 4.  On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach.

Authors:  Sangsoo Lim; Sangseon Lee; Yinhua Piao; MinGyu Choi; Dongmin Bang; Jeonghyeon Gu; Sun Kim
Journal:  Comput Struct Biotechnol J       Date:  2022-08-05       Impact factor: 6.155

5.  Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm.

Authors:  Song Ge; Chen-Rui Xu; Yan-Ming Li; Yu-Lin Zhang; Na Li; Fei-Tong Wang; Liang Ding; Jian Niu
Journal:  J Oncol       Date:  2022-09-15       Impact factor: 4.501

  5 in total

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