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Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks.
Sun, Ping; Wang, Xiangwen; Wang, Shenghai; Jia, Xueyu; Feng, Shunkang; Chen, Jun; Fang, Yiru.
Afiliación
  • Sun P; Qingdao Mental Health Center, Shandong 266034, China.
  • Wang X; Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
  • Wang S; Qingdao Mental Health Center, Shandong 266034, China.
  • Jia X; School of Mental Health, Research Institute of Mental Health,Jining Medical University, Shandong 272002, China.
  • Feng S; Qingdao Mental Health Center, Shandong 266034, China.
  • Chen J; Department of Medicine,Qingdao University, Shandong 266000, China.
  • Fang Y; Qingdao Mental Health Center, Shandong 266034, China.
IBRO Neurosci Rep ; 17: 145-153, 2024 Dec.
Article en En | MEDLINE | ID: mdl-39206162
ABSTRACT

Background:

To construct a diagnostic model for Bipolar Disorder (BD) depressive phase using peripheral tissue RNA data from patients and combining Random Forest with Feedforward Neural Network methods.

Methods:

Datasets GSE23848, GSE39653, and GSE69486 were selected, and differential gene expression analysis was conducted using the limma package in R. Key genes from the differentially expressed genes were identified using the Random Forest method. These key genes' expression levels in each sample were used to train a Feedforward Neural Network model. Techniques like L1 regularization, early stopping, and dropout layers were employed to prevent model overfitting. Model performance was then validated, followed by GO, KEGG, and protein-protein interaction network analyses.

Results:

The final model was a Feedforward Neural Network with two hidden layers and two dropout layers, comprising 2345 trainable parameters. Model performance on the validation set, assessed through 1000 bootstrap resampling iterations, demonstrated a specificity of 0.769 (95 % CI 0.571-1.000), sensitivity of 0.818 (95 % CI 0.533-1.000), AUC value of 0.832 (95 % CI 0.642-0.979), and accuracy of 0.792 (95 % CI 0.625-0.958). Enrichment analysis of key genes indicated no significant enrichment in any known pathways.

Conclusion:

Key genes with biological significance were identified based on the decrease in Gini coefficient within the Random Forest model. The combined use of Random Forest and Feedforward Neural Network to establish a diagnostic model showed good classification performance in Bipolar Disorder.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IBRO Neurosci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IBRO Neurosci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos