| bibtype |
J -
Journal Article
|
| ARLID |
0477168 |
| utime |
20240103214409.8 |
| mtime |
20170820235959.9 |
| SCOPUS |
85009223244 |
| WOS |
000407655600031 |
| DOI |
10.1016/j.ijar.2016.12.012 |
| title
(primary) (eng) |
An empirical comparison of popular structure learning algorithms with a view to gene network inference |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0256774 |
| ISSN |
0888-613X |
| title
|
International Journal of Approximate Reasoning |
| volume_id |
88 |
| volume |
1 (2017) |
| page_num |
602-613 |
| publisher |
|
|
| keyword |
Bayesian networks |
| keyword |
Structure learning |
| keyword |
Reverse engineering |
| keyword |
Gene networks |
| author
(primary) |
| ARLID |
cav_un_auth*0322154 |
| name1 |
Djordjilović |
| name2 |
V. |
| country |
IT |
|
| author
|
| ARLID |
cav_un_auth*0322155 |
| name1 |
Chiogna |
| name2 |
M. |
| country |
IT |
|
| author
|
| ARLID |
cav_un_auth*0101228 |
| name1 |
Vomlel |
| name2 |
Jiří |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0332303 |
| project_id |
GA16-12010S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
In this work, we study the performance of different structure learning algorithms in the context of inferring gene networks from transcription data. We consider representatives of different structure learning approaches, some of which perform unrestricted searches, such as the PC algorithm and the Gobnilp method, and some of which introduce prior information on the structure, such as the K2 algorithm. Competing methods are evaluated both in terms of their predictive accuracy and their ability to reconstruct the true underlying network. Areal data application based on an experiment performed by the University of Padova is also considered. |
| RIV |
JD |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2018 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0273649 |
| mrcbC62 |
1 |
| confidential |
S |
| mrcbC86 |
3+4 Article|Proceedings Paper Computer Science Artificial Intelligence |
| mrcbC86 |
3+4 Article|Proceedings Paper Computer Science Artificial Intelligence |
| mrcbC86 |
3+4 Article|Proceedings Paper Computer Science Artificial Intelligence |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.658 |
| mrcbT16-s |
0.866 |
| mrcbT16-B |
44.33 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q2 |
| arlyear |
2017 |
| mrcbU14 |
85009223244 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000407655600031 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 88 č. 1 2017 602 613 Elsevier |
|