Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.
Bioinformatics
; 34(11): 1884-1892, 2018 06 01.
Article
em En
| MEDLINE
| ID: mdl-29390084
ABSTRACT
Motivation Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies. Results:
PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi-a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations. Availability and implementation https//github.com/ima23/maxent-ppi. Contact sbh11@cl.cam.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Biologia Computacional
/
Anotação de Sequência Molecular
/
Máquina de Vetores de Suporte
/
Ontologia Genética
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Reino Unido