TY - JOUR
T1 - An integrated comprehensive workbench for inferring genetic networks
T2 - VoyaGene
AU - Maki, Yukihiro
AU - Takahashi, Yoriko
AU - Arikawa, Yuji
AU - Watanabe, Shoji
AU - Aoshima, Ken
AU - Eguchi, Yukihiro
AU - Ueda, Takanori
AU - Aburatani, Sachiyo
AU - Kuhara, Satoru
AU - Okamoto, Masahiro
N1 - Funding Information:
This study was supported by Grant-in-Aid for the research and development project of Industrial Science and Technology Program from NEDO (New Energy and Industrial Technology Development Organization), Japan (MKI:1998–2002) and by Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Science and Technology, Japan (No.12208008 (M. Okamoto)).
PY - 2004/9
Y1 - 2004/9
N2 - We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.
AB - We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.
UR - http://www.scopus.com/inward/record.url?scp=4644242482&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=4644242482&partnerID=8YFLogxK
U2 - 10.1142/S0219720004000727
DO - 10.1142/S0219720004000727
M3 - Article
C2 - 15359425
AN - SCOPUS:4644242482
SN - 0219-7200
VL - 2
SP - 533
EP - 550
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
IS - 3
ER -