Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.
Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.
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DOI：
10.1093/gigascience/gix127
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发表时间：
2018-02-01
期刊：
影响因子：
9.2
通讯作者：
Guan Y
中科院分区：
文献类型：
--
作者：
Li H;Panwar B;Omenn GS;Guan Y
嗅觉刺激-嗅觉感受问题已经被研究了世纪，但是由于气味分子的大规模化学信息学特征，仍然很难精确地预测气味。一个主要的挑战是，由于不同的遗传和文化背景，个人之间的感知质量差异很大。此外，多种气味受体和不同分子之间的组合相互作用显着复杂的嗅觉预测。许多人试图建立结构-气味的强度和愉悦性关系，但没有模型可用于预测分子的个性化多气味属性。在这项研究中，我们描述了我们的获奖算法预测个人和人口的感知反应，各种气味的梦想嗅觉预测挑战。我们发现，随机森林模型组成的多个决策树是非常适合这个预测问题，考虑到大的特征空间和高变异性的个人之间的感知评级。将群体和个人的感知整合到我们的模型中，有效地减少了噪音和离群值的影响。通过分析每个化学特征的重要性，我们发现一小组低变性和非变性特征足以进行准确预测。我们的随机森林模型成功地预测了结构多样化分子的个性化气味属性。该模型与最高判别特征一起有可能扩展我们对嗅觉感知机制的理解，并为合理的气味设计提供替代方案。
The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.
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