Title
Completing SBGN-AF Networks by Logic-Based Hypothesis Finding.
Abstract
This study considers formal methods for finding unknown interactions of incomplete molecular networks using microarray profiles. In systems biology, a challenging problem lies in the growing scale and complexity of molecular networks. Along with high-throughput experimental tools, it is not straightforward to reconstruct huge and complicated networks using observed data by hand. Thus, we address the completion problem of our target networks represented by a standard markup language, called SBGN (in particular, Activity Flow). Our proposed method is based on logic-based hypothesis finding techniques; given an input SBGN network and its profile data, missing interactions can be logically generated as hypotheses by the proposed method. In this paper, we also show empirical results that demonstrate how the proposed method works with a real network involved in the glucose repression of S. cerevisiae.
Year
DOI
Venue
2014
10.1007/978-3-319-10398-3_14
Lecture Notes in Bioinformatics
Keywords
Field
DocType
completion,hypothesis finding,SBGN,glucose repression
Computer science,Systems biology,Theoretical computer science,Glucose repression,Formal methods,Markup language
Conference
Volume
ISSN
Citations 
8738
0302-9743
1
PageRank 
References 
Authors
0.43
8
7
Name
Order
Citations
PageRank
Yoshitaka Yamamoto1297.50
Adrien Rougny222.16
Hidetomo Nabeshima315414.88
Katsumi Inoue41271112.78
Hisao Moriya530.78
Christine Froidevaux621.14
Koji Iwanuma713817.65