DocumentCode :
2257752
Title :
Genetic regulatory networks established by shortest path algorithm and conditional probabilities for ovarian carcinoma microarray data
Author :
Tsai, Meng-Hsiun ; Ko, Hsiao-Han ; Chiu, Sheng-chuan
Author_Institution :
Dept. of Manage. Inf. Syst., Nat. Chung Hsing Univ., Taichung, Taiwan
Volume :
1
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
32
Lastpage :
36
Abstract :
In the cancer research recently, it still doesn´t have a definitive conclusion for the regulatory mechanisms of tumorigenesis and metastasis. But different genes have different biological functions, and these functions with interactions between genes play an important key in gene regulatory networks. Microarray is a tool most commonly used in the disease research, and scientists usually use that the feature can accommodate huge data to record gene expressions in cancer. Then we will apply target genes to identify regulatory pathways by Dijkstra´s algorithm combined with Bayesian theorem. There are 18 regulatory pathways are identified by this hybrid methods, such as chemokine signaling pathway, cell cycle and apoptosis successfully, moreover these results show the inhibition or activation between genes as well as their directions with validation by databases. This hybrid method can not only analyze complex cancer pathways, but also it will be helpful to find a more effective treatment for disease research in the future.
Keywords :
Bayes methods; cancer; data handling; genetics; graph theory; medical information systems; probability; Bayesian theorem; Dijkstra algorithm; apoptosis; biological function; cell cycle; chemokine signaling pathway; complex cancer pathway; conditional probability; disease; gene expression; genetic regulatory network; metastasis; ovarian carcinoma microarray data; shortest path algorithm; tumorigenesis; Bayesian methods; Bioinformatics; Breast cancer; Gene expression; Machine learning; Bayesian theorem; Dijkstra´s algorithm; Gene regulatory network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
Type :
conf
DOI :
10.1109/ICMLC.2010.5581099
Filename :
5581099
Link To Document :
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