Selected article for: "activity relationship and adaboost learner nearest neighbor algorithm"
Author: Hu, Le-Le; Chen, Chen; Huang, Tao; Cai, Yu-Dong; Chou, Kuo-Chen
Title: Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions
  • Document date: 2011_12_29
    • ID: 0mn1gbll_2
    Snippet: Besides the conventional biochemical experiments, computational methods are alternative ways to annotate the biological functions of compounds. In recent years, various bioinformatics and structural bioinformatics [6] tools were developed to address this issue, such as Quantitative Structure Activity Relationship (QSAR) [7, 8] , pharmacophore modeling [9] , molecular docking [10] , and Monte Carlo simulated annealing approach [11, 12] . Different.....
    Document: Besides the conventional biochemical experiments, computational methods are alternative ways to annotate the biological functions of compounds. In recent years, various bioinformatics and structural bioinformatics [6] tools were developed to address this issue, such as Quantitative Structure Activity Relationship (QSAR) [7, 8] , pharmacophore modeling [9] , molecular docking [10] , and Monte Carlo simulated annealing approach [11, 12] . Different from these methods, Lu et al. [1] and Cai et al. [2] analyzed the biological functions of compounds by mapping them to the corresponding metabolic pathway classes, which are strongly associated with the biological functions of compounds. The functional group composition was used to represent the compounds, and the Nearest Neighbor Algorithm and AdaBoost learner [13] were used to construct the prediction models by Cai et al. [2] and Lu et al. [1] , respectively. Both the two prediction methods achieved quite promising results on their own datasets.

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