Detecting protein complexes with multiple properties by an adaptive harmony search algorithm.
BMC Bioinformatics
; 23(1): 414, 2022 Oct 07.
Article
en En
| MEDLINE
| ID: mdl-36207692
ABSTRACT
BACKGROUND:
Accurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks.RESULT:
To address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm's parameters automatically.CONCLUSIONS:
Experimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Mapeo de Interacción de Proteínas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China