| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T1 |
101-166 |
DRI_Background |
denotes |
Herbal formulae have a long history in clinical medicine in Asia. |
| T2 |
167-403 |
DRI_Challenge |
denotes |
While the complexity of the formulae leads to the complex compound-target interactions and the resultant multi-target therapeutic effects, it is difficult to elucidate the molecular/therapeutic mechanism of action for the many formulae. |
| T3 |
404-546 |
DRI_Background |
denotes |
For example, the Hua-Yu-Qiang-Shen-Tong-Bi-Fang (TBF), an herbal formula of Chinese medicine, has been used for treating rheumatoid arthritis. |
| T4 |
547-642 |
DRI_Approach |
denotes |
However, the target information of a great number of compounds from the TBF formula is missing. |
| T5 |
643-766 |
DRI_Approach |
denotes |
In this study, we predicted the targets of the compounds from the TBF formula via network analysis and in silico computing. |
| T6 |
767-989 |
DRI_Outcome |
denotes |
Initially, the information of the phytochemicals contained in the plants of the herbal formula was collected, and subsequently computed to their corresponding fingerprints for the sake of structural similarity calculation. |
| T7 |
990-1094 |
DRI_Background |
denotes |
Then a compound structural similarity network infused with available target information was constructed. |
| T8 |
1095-1227 |
DRI_Background |
denotes |
Five local similarity indices were used and compared for their performance on predicting the potential new targets of the compounds. |
| T9 |
1228-1425 |
DRI_Outcome |
denotes |
Finally, the Preferential Attachment Index was selected for it having an area under curve (AUC) of 0.886, which outperforms the other four algorithms in predicting the compound-target interactions. |
| T10 |
1426-1552 |
DRI_Background |
denotes |
This method could provide a promising direction for identifying the compound-target interactions of herbal formulae in silico. |
