A Learning Based Framework for Disease Prediction from Images of Human-Derived Pluripotent Stem Cells of Schizophrenia Patients.
Neuroinformatics
; 20(2): 513-523, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-35064871
ABSTRACT
Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ) patients. We show that, by using Support Vector Machines or selected Artificial Neural Networks trained on image-based features associated with somas of hiPSCs derived neurons, we can predict very reliably SCZ and healthy control cells. In addition, our method reveals that [Formula see text]III tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Esquizofrenia
/
Células-Tronco Pluripotentes
/
Células-Tronco Pluripotentes Induzidas
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Neuroinformatics
Assunto da revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos