Function-Genes and Disease-Genes Prediction Based on Network Embedding and One-Class Classification.
Interdiscip Sci
; 2024 Sep 04.
Article
in En
| MEDLINE
| ID: mdl-39230798
ABSTRACT
Using genes which have been experimentally-validated for diseases (functions) can develop machine learning methods to predict new disease/function-genes. However, the prediction of both function-genes and disease-genes faces the same problem:
there are only certain positive examples, but no negative examples. To solve this problem, we proposed a function/disease-genes prediction algorithm based on network embedding (Variational Graph Auto-Encoders, VGAE) and one-class classification (Fast Minimum Covariance Determinant, Fast-MCD) VGAEMCD. Firstly, we constructed a protein-protein interaction (PPI) network centered on experimentally-validated genes; then VGAE was used to get the embeddings of nodes (genes) in the network; finally, the embeddings were input into the improved deep learning one-class classifier based on Fast-MCD to predict function/disease-genes. VGAEMCD can predict function-gene and disease-gene in a unified way, and only the experimentally-verified genes are needed to provide (no need for expression profile). VGAEMCD outperforms classical one-class classification algorithms in Recall, Precision, F-measure, Specificity, and Accuracy. Further experiments show that seven metrics of VGAEMCD are higher than those of state-of-art function/disease-genes prediction algorithms. The above results indicate that VGAEMCD can well learn the distribution characteristics of positive examples and accurately identify function/disease-genes.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Interdiscip Sci
/
Interdiscip. sci.: comput. life sci. (Internet)
/
Interdisciplinary sciences: computational life sciences (Internet)
Journal subject:
BIOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: