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.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 165-179 |
| Number of pages | 15 |
| Volume | 8738 LNBI |
| ISBN (Print) | 9783319103976 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 1st International Conference on Formal Methods in Macro-Biology, FMMB 2014 - Noumea, New Caledonia Duration: Sep 22 2014 → Sep 24 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8738 LNBI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 1st International Conference on Formal Methods in Macro-Biology, FMMB 2014 |
|---|---|
| Country | New Caledonia |
| City | Noumea |
| Period | 9/22/14 → 9/24/14 |
Fingerprint
Keywords
- completion
- glucose repression
- hypothesis finding
- SBGN
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Completing SBGN-AF networks by logic-based hypothesis finding. / Yamamoto, Yoshitaka; Rougny, Adrien; Nabeshima, Hidetomo; Inoue, Katsumi; Moriya, Hisao; Froidevaux, Christine; Iwanuma, Koji.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8738 LNBI Springer Verlag, 2014. p. 165-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8738 LNBI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Completing SBGN-AF networks by logic-based hypothesis finding
AU - Yamamoto, Yoshitaka
AU - Rougny, Adrien
AU - Nabeshima, Hidetomo
AU - Inoue, Katsumi
AU - Moriya, Hisao
AU - Froidevaux, Christine
AU - Iwanuma, Koji
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - completion
KW - glucose repression
KW - hypothesis finding
KW - SBGN
UR - http://www.scopus.com/inward/record.url?scp=84906334379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906334379&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10398-3-14
DO - 10.1007/978-3-319-10398-3-14
M3 - Conference contribution
SN - 9783319103976
VL - 8738 LNBI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 165
EP - 179
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -