Publication Type |
Journal Article |
School or College |
College of Engineering |
Department |
Electrical & Computer Engineering |
Creator |
Myers, Chris J. |
Other Author |
Barker, Nathan; Kuwahara, Hiroyuki |
Title |
Learning genetic regulatory network connectivity from time series data |
Date |
2006 |
Description |
Abstract. Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate the genetic regulatory network inferred from time series data. Our method fi_x000C_rst encodes the data into levels. Next, it determines the set of potential parents for each gene based upon the probability of the gene's expression increasing. After a subset of potential parents are selected, it determines if any genes in this set may have a combined eff_x000B_ect. Finally, the potential sets of parents are competed against each other to determine the fi_x000C_nal set of parents. The result is a directed graph representation of the genetic network's repression and activation connections. Our results on synthetic data generated from models for several genetic networks with tight feedback are promising. |
Type |
Text |
Publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
First Page |
2 |
Last Page |
10 |
Language |
eng |
Bibliographic Citation |
Barker, N., Myers, C., & Kuwahara, H. (2006). Learning genetic regulatory network connectivity from time series data. The 19th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems (IEA/AIE06), 0331270, 2-10. June. |
Rights Management |
(c) 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
Format Medium |
application/pdf |
Format Extent |
145,246 bytes |
Identifier |
ir-main,15008 |
ARK |
ark:/87278/s6zp4qh1 |
Setname |
ir_uspace |
ID |
704608 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6zp4qh1 |