An Artificial Neural Network Approach for Glomerular Activity Pattern Prediction Using the Graph Kernel Method and the Gaussian Mixture Functions
An Artificial Neural Network Approach for Glomerular Activity Pattern Prediction Using the Graph Kernel Method and the Gaussian Mixture Functions
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DOI：
10.1093/chemse/bjq147
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发表时间：
2011-06-01
期刊：
影响因子：
3.5
通讯作者：
Ohtake, Hisao
中科院分区：
文献类型：
--
作者：
Soh, Zu;Tsuji, Toshio;Ohtake, Hisao
本文提出了一种神经网络模型预测嗅球的活动，旨在未来的应用，气味质量的评价。该模型的输入是表示为标记图的气味分子的结构，并且它采用图核方法来量化气味和嗅觉受体神经元的功能之间的结构相似性。然后，人工神经网络将气味分子转换为以高斯混合函数表示的肾小球活动。作者还提出了一种学习算法，该算法允许使用由成对的气味剂和测量的肾小球活动模式组成的学习数据集来调整模型中包含的参数。我们观察到，气味结构之间的定义的相似性与肾小球活动的相关性为0.3-0.9。肾小球活动预测模拟显示出一定水平的预测能力，其中预测的肾小球活动模式也与测量的肾小球活动模式相关，对于包含363种气味剂的数据集，平均具有中到高的相关性。
This paper proposes a neural network model for prediction of olfactory glomerular activity aimed at future application to the evaluation of odor qualities. The model's input is the structure of an odorant molecule expressed as a labeled graph, and it employs the graph kernel method to quantify structural similarities between odorants and the function of olfactory receptor neurons. An artificial neural network then converts odorant molecules into glomerular activity expressed in Gaussian mixture functions. The authors also propose a learning algorithm that allows adjustment of the parameters included in the model using a learning data set composed of pairs of odorants and measured glomerular activity patterns. We observed that the defined similarity between odorant structure has correlation of 0.3-0.9 with that of glomerular activity. Glomerular activity prediction simulation showed a certain level of prediction ability where the predicted glomerular activity patterns also correlate the measured ones with middle to high correlation in average for data sets containing 363 odorants.