...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Sensors and Actuators >Odorant clustering based on molecular parameter-feature extraction and imaging analysis of olfactory bulb odor maps
【24h】

Odorant clustering based on molecular parameter-feature extraction and imaging analysis of olfactory bulb odor maps

机译：基于分子参数特征的气味聚类-嗅球气味图的成像分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Progress in the molecular biology of olfaction has revealed a close relationship between the structural features of odorants and the response patterns they elicit in the olfactory bulb. Molecular feature-related response patterns, termed odor maps (OMs), may represent information related to basic odor quality. Thus, studying the relationship between OMs and the molecular features of odorants is helpful for better understanding the relationships between odorant structure and odor. Here, we explored the correlation between OMs and the molecular parameters (MPs) of odorants by taking OMs from rat olfactory bulbs and extracting feature profiles of the corresponding odorant molecules. 178 images of glomerular activities in olfactory bulb that are corresponding to odorants were taken from the OdorMapDB, a publicly accessible database. The gray value of each pixel was extracted from the images (178 × 357 pixels) to fabricate an image matrix for each odorant. Forty-six molecular feature parameters were calculated using BioChem3D software, which was used to construct a second matrix for each odorant. Correlation analysis between the two matrixes was first carried out by establishing coefficient maps. Results from hierarchical clustering showed that all parameters could be segregated into seven clusters, and each cluster showed a relatively similar response pattern in the olfactory bulb. Using the information from the OMs and MPs, we mapped odorants in 2D space by incorporating dimension-reducing techniques based on principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). Artificial neural network models based on the OM and MP feature values were proposed as a means to identify odorant functional groups. An OM-PCA-based model calibrated via extreme learning machine (ELM) was 94.81% and 93.02% accuracy for the calibration and validation sets, respectively. Similarly, an MP-t-SNE-based model calibrated by ELM was 86.67% and 93.35% accuracy for the calibration set and the validation set, respectively. Thus, this research supports a structure-odor relationship from a data-analysis perspective.
机译：嗅觉的分子生物学进展表明，气味剂的结构特征与其在嗅球中引起的反应模式之间存在密切的关系。与分子特征有关的响应模式，称为气味图（OMs），可能表示与基本气味质量有关的信息。因此，研究OM与气味剂分子特征之间的关系有助于更好地理解气味剂结构与气味之间的关系。在这里，我们通过从大鼠嗅球中提取OMs并提取相应气味剂分子的特征图谱，探索了OMs与气味剂分子参数（MPs）之间的相关性。从可公开访问的数据库OdorMapDB上获取了178种与气味有关的嗅球中肾小球活动的图像。从图像（178×357像素）中提取每个像素的灰度值，以制作每个加味剂的图像矩阵。使用BioChem3D软件计算了46个分子特征参数，该软件用于为每种加味剂构建第二个基质。首先通过建立系数图来进行两个矩阵之间的相关性分析。分层聚类的结果表明，所有参数都可以分为七个聚类，每个聚类在嗅球中显示出相对相似的响应模式。利用来自OM和MP的信息，我们通过结合基于主成分分析（PCA）和t分布随机邻居嵌入（t-SNE）的降维技术，在二维空间中绘制了气味剂。提出了基于OM和MP特征值的人工神经网络模型，作为识别气味功能基团的一种手段。通过极限学习机（ELM）校准的基于OM-PCA的模型的校准和验证集的准确性分别为94.81％和93.02％。同样，ELM校准的基于MP-t-SNE的模型的校准集和验证集的准确度分别为86.67％和93.35％。因此，这项研究从数据分析的角度支持了结构与气味的关系。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号