BENIN: Biologically enhanced network inference.
J Bioinform Comput Biol
; 18(3): 2040007, 2020 06.
Article
in En
| MEDLINE
| ID: mdl-32698722
ABSTRACT
Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference. The method states the network inference as a feature selection problem and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it. BENIN significantly outperforms the state-of-the-art methods on the simulated data from the DREAM 4 challenge when combining genome-wide location data, knockout gene expression data, and time series expression data.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Computational Biology
/
Gene Regulatory Networks
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
J Bioinform Comput Biol
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2020
Document type:
Article
Affiliation country:
Canada