Network controllability solutions for computational drug repurposing using genetic algorithms.
Sci Rep
; 12(1): 1437, 2022 01 26.
Article
em En
| MEDLINE
| ID: mdl-35082323
ABSTRACT
Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdos-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
/
2_ODS3
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Ovarianas
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Neoplasias Pancreáticas
/
Algoritmos
/
Neoplasias da Mama
/
Reposicionamento de Medicamentos
/
Proteínas de Neoplasias
/
Antineoplásicos
Tipo de estudo:
Prognostic_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2022
Tipo de documento:
Article