Network-based prediction of polygenic disease genes involved in cell motility.
BMC Bioinformatics
; 20(Suppl 12): 313, 2019 Jun 20.
Article
em En
| MEDLINE
| ID: mdl-31216978
ABSTRACT
BACKGROUND:
Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases.RESULTS:
We formulate the POLYGENIC DISEASE PHENOTYPE Problem which seeks to identify candidate disease genes that may be associated with a phenotype such as cell motility. We present a machine learning approach to solve this problem for schizophrenia and autism genes within a brain-specific functional interaction network. Our method outperforms peer semi-supervised learning approaches, achieving better cross-validation accuracy across different sets of gold-standard positives. We identify top candidates for both schizophrenia and autism, and select six genes labeled as schizophrenia positives that are predicted to be associated with cell motility for follow-up experiments.CONCLUSIONS:
Candidate genes predicted by our method suggest testable hypotheses about these genes’ role in cell motility regulation, offering a framework for generating predictions for experimental validation.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Movimento Celular
/
Doença
/
Herança Multifatorial
/
Redes Reguladoras de Genes
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article