Scaling structural learning with NO-BEARS to infer causal transcriptome networks.
Pac Symp Biocomput
; 25: 391-402, 2020.
Article
en En
| MEDLINE
| ID: mdl-31797613
ABSTRACT
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NO-TEARS algorithm with two improvements. First, we propose a new constraint and its fast approximation to reduce the computational cost of the NO-TEARS algorithm. Next, we introduce a polynomial regression loss to handle non-linearity in gene expressions. Our implementation utilizes modern GPU computation that can decrease the time of hours-long CPU computation to seconds. Using synthetic data, we demonstrate improved performance, both in processing time and accuracy, on inferring gene regulatory networks from gene expression data.
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Banco de datos:
MEDLINE
Asunto principal:
Ursidae
/
Transcriptoma
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Pac Symp Biocomput
Asunto de la revista:
BIOTECNOLOGIA
/
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos