Abstract:
:Adverse drug-drug interaction (ADDI) becomes a significant threat to public health. Despite the detection of ADDIs is experimentally implemented in the early development phase of drug design, many potential ADDIs are still clinically explored by accidents, leading to a large number of morbidity and mortality. Several computational models are designed for ADDI prediction. However, they take no consideration of drug dependency, although many drugs usually produce synergistic effects and own highly mutual dependency in treatments, which contains underlying information about ADDIs and benefits ADDI prediction. In this paper, we design a dependent network to model the drug dependency and propose an attribute supervised learning model Probabilistic Dependent Matrix Tri-Factorization (PDMTF) for ADDI prediction. In particular, PDMTF incorporates two drug attributes, molecular structure and side effect, and their correlation to model the adverse interactions among drugs. The dependent network is represented by a dependent matrix, which is first formulated by the row precision matrix of the predicted attribute matrices and then regularized by the molecular structure similarities among drugs. Meanwhile, an efficient alternating algorithm is designed for solving the optimization problem of PDMTF. Experiments demonstrate the superior performance of the proposed model when compared with eight baselines and its two variants.
journal_name
IEEE J Biomed Health Informjournal_title
IEEE journal of biomedical and health informaticsauthors
Zhu J,Liu Y,Zhang Y,Li Ddoi
10.1109/JBHI.2020.3048059subject
Has Abstractpub_date
2020-12-29 00:00:00eissn
2168-2194issn
2168-2208journal_volume
PPpub_type
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