DocumentCode
1599772
Title
Application of Stochastic Proximity Embedding to Distance Geometry Problems
Author
Kashima, Hiroyuki ; DOI, Shinji ; Kumagai, Sadatoshi
Author_Institution
Graduate Sch. of Eng., Osaka Univ.
fYear
2006
Firstpage
4451
Lastpage
4456
Abstract
We extend the stochastic proximity embedding (SPE) method which was proposed as a method of data-mining, and apply it to distance geometry problems. Distance geometry problems are the problem that we calculate the coordinates of atoms from the distance data between atoms. We also propose an improvement of SPE and demonstrate its effectiveness in determining protein structures
Keywords
biology computing; data mining; data visualisation; geometry; molecular biophysics; molecular configurations; proteins; data visualization; data-mining; distance geometry problems; protein structures; stochastic proximity embedding; Data engineering; Euclidean distance; Geometry; Machine learning; Magnetic analysis; Nuclear magnetic resonance; Optimization methods; Proteins; Statistics; Stochastic processes; data-mining; nuclear magnetic resonance analysis; optimization method; protein-structure determination;
fLanguage
English
Publisher
ieee
Conference_Titel
SICE-ICASE, 2006. International Joint Conference
Conference_Location
Busan
Print_ISBN
89-950038-4-7
Electronic_ISBN
89-950038-5-5
Type
conf
DOI
10.1109/SICE.2006.314780
Filename
4108301
Link To Document