Title :
Link prediction of complex networks from temporal quantity variation assigned to elements
Author :
Maeshiro, Tetsuya ; Hemmi, Hitoshi ; Nakayama, Shin ichi ; Shimohara, Katsunori
Author_Institution :
Sch. of Libr. & Inf. Sci., Univ. of Tsukuba, Tsukuba
Abstract :
This paper presents a method to predict gene regulatory networks from gene expression time course data, where each gene has multiple types of quantity values and each quantity type might be influenced by all other quantity types. This problem is considerably harder than the prediction from time course data of only one quantity type. The method is based on a ultra high speed gene network simulator Starpack and evolutionary mechanisms. The prediction is a combination of two stage loop, each stage using evolutionary mechanism. Networks are simulated with Starpack, and those producing time course data similar to the target gene expression data are selected as candidates. The simulation of the second stage has higher precision than the first stage, serving as local optimization process. A synthetic network was used for evaluation, and results suggest necessity of improvements.
Keywords :
data mining; evolutionary computation; complex networks; evolutionary mechanism; gene expression time course data; gene network simulator Starpack; gene regulatory network prediction; link prediction; local optimization process; synthetic network; temporal quantity variation; Biological systems; Complex networks; Economic forecasting; Electronic mail; Gene expression; Information science; Laboratories; Libraries; Proteins; Time measurement; Gene regulatory network; gene expression; network inference; simulation;
Conference_Titel :
SICE Annual Conference, 2008
Conference_Location :
Tokyo
Print_ISBN :
978-4-907764-30-2
Electronic_ISBN :
978-4-907764-29-6
DOI :
10.1109/SICE.2008.4654896