TY - JOUR
T1 - Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion
AU - Liu, Shuaiqi
AU - An, Jingjie
AU - Zhao, Jie
AU - Zhao, Shuhuan
AU - Lv, Hui
AU - Wang, Shuihua
N1 - Publisher Copyright:
© 2021 Shuaiqi Liu et al.
PY - 2021
Y1 - 2021
N2 - Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.
AB - Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.
UR - http://www.scopus.com/inward/record.url?scp=85120824829&partnerID=8YFLogxK
U2 - 10.1155/2021/6044256
DO - 10.1155/2021/6044256
M3 - Article
C2 - 34908912
AN - SCOPUS:85120824829
SN - 1555-4309
VL - 2021
JO - Contrast Media and Molecular Imaging
JF - Contrast Media and Molecular Imaging
M1 - 6044256
ER -