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Table 2 Case study results

From: Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction

CUI Disease name CUI Disease
C0575081 Gait abnormality CUI:C0678230 Congenital Epicanthus
Gene Original DB Gene Original DB
FGFR3   FBN1  
POU1F1   IKBKG  
ROR2   LBR  
GRM1   KCNJ2  
OFD1 DisGeNet* ROR2 DisGeNet
BMPR1B   SOX9  
FGFR2   FLNA DisGeNet*
RPS29   GDF5  
IL6   LMNA  
SLC9A6   TBX3  
MYH6   FOXG1 DisGeNet
RAF1   OFD1 DisGeNet
COL6A2   HDAC6  
MKKS   GRM1  
MAP3K7   TGDS  
PTCH2   WDR60 DisGeNet
KCNJ2   GJA1 DisGeNet
RAG1   OAT  
LMNA DisGeNet ZMPSTE24  
FAS DisGeNet TREX1  
  1. In the prediction results of the above table, the candidate genes with known association were labeled in the original DB, and candidate genes marked with "*" indicate newly discovered associated genes, that is, there are not exist in dataset but records in the latest online database. The results show that our model has the ability to mine new disease gene associations, such as OFD1-C057508 and FLNA-C0678230. Our model does not remember the existing associations in the original dataset, but predicts new candidate genes by mining the hidden patterns. This is very important, because it is difficult to mine new genes only by making a high score for the known associations. Therefore, our model can help to decipher the relationship between diseases and genes, which has certain biomedical significance