Thumbnail
Access Restriction
Subscribed

Author Harada, Y. ♦ Kazawa, T. ♦ Kanzaki, R. ♦ Nakamoto, T.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Structural engineering ♦ Accuracy ♦ Approximation methods ♦ Training ♦ Olfactory ♦ Neurons ♦ Prediction methods ♦ insert (key words) ♦ component ♦ formatting ♦ style ♦ styling
Abstract Our group has for years been studying odor approximation to express a variety of odors using a small number of odor components. The insect's olfaction is more appropriate for systematic survey than mammal's one because of its database's availability. Thus, we propose prediction method of Drosophila's ORN(Olfactory Receptor Neuron) response using structural parameters of an odorant molecule and SOM (Self-Organizing Map) mapping. We obtained ~5000 structural parameters of odorant molecules by using Dragon (Talete s.r.l.). Then, SOM maps structural parameters of an odorant molecule onto an OR response to corresponding odor. As a result, the ORN response prediction was roughly performed. For improving accuracy, we selected important parameters based on the condition number. Then, the simulation suggests the prediction can be performed only using a few tens of parameters.
Description Author affiliation: Tokyo Inst. of Technol., Yokohama, Japan (Harada, Y.; Nakamoto, T.) || Univ. of Tokyo, Tokyo, Japan (Kazawa, T.; Kanzaki, R.)
ISBN 9781479901623
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-11-02
Publisher Place Spain
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 950.79 kB
Page Count 4
Starting Page 1014
Ending Page 1017


Source: IEEE Xplore Digital Library