Related references
Note: Only part of the references are listed.Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Xuan Lin et al.
BRIEFINGS IN BIOINFORMATICS (2023)
MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
Shenggeng Lin et al.
BRIEFINGS IN BIOINFORMATICS (2022)
Drug-drug interaction prediction with learnable size-adaptive molecular substructures
Arnold K. Nyamabo et al.
BRIEFINGS IN BIOINFORMATICS (2022)
Multi-type feature fusion based on graph neural network for drug-drug interaction prediction
Changxiang He et al.
BMC BIOINFORMATICS (2022)
STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions
Hui Yu et al.
BRIEFINGS IN BIOINFORMATICS (2022)
Prediction of drug-drug interaction events using graph neural networks based feature extraction
Mohammad Hussain Al-Rabeah et al.
SCIENTIFIC REPORTS (2022)
Long-term hybrid prediction method based on multiscale decomposition and granular computing for oxygen supply network
Pengwei Zhou et al.
COMPUTERS & CHEMICAL ENGINEERING (2021)
A neural network-based method for polypharmacy side effects prediction
Raziyeh Masumshah et al.
BMC BIOINFORMATICS (2021)
MedFused: A framework to discover the relationships between drug chemical functional group impacts and side effects
M. A. P. Chamikara et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2021)
Three-way granular computing, rough sets, and formal concept analysis
Yiyu Yao
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING (2020)
A multimodal deep learning framework for predicting drug-drug interaction events
Yifan Deng et al.
BIOINFORMATICS (2020)
Deep multi-granularity graph embedding for user identity linkage across social networks
Shun Fu et al.
KNOWLEDGE-BASED SYSTEMS (2020)
MTMA: Multi-task multi-attribute learning for the prediction of adverse drug-drug interaction
Jiajing Zhu et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Novel deep learning model for more accurate prediction of drug-drug interaction effects
Geonhee Lee et al.
BMC BIOINFORMATICS (2019)
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
Wen Zhang et al.
BMC BIOINFORMATICS (2017)
Co-Prescription Trends in a Large Cohort of Subjects Predict Substantial Drug-Drug Interactions
Jeffrey J. Sutherland et al.
PLOS ONE (2015)
Granular Computing: Perspectives and Challenges
JingTao Yao et al.
IEEE TRANSACTIONS ON CYBERNETICS (2013)
Tensor factorization using auxiliary information
Atsuhiro Narita et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2012)
INDI: a computational framework for inferring drug interactions and their associated recommendations
Assaf Gottlieb et al.
MOLECULAR SYSTEMS BIOLOGY (2012)
Data-Driven Prediction of Drug Effects and Interactions
Nicholas P. Tatonetti et al.
SCIENCE TRANSLATIONAL MEDICINE (2012)
Tiering Drug-Drug Interaction Alerts by Severity Increases Compliance Rates
Marilyn D. Paterno et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2009)
In vitro safety pharmacology profiling:: an essential tool for successful drug development
S Whitebread et al.
DRUG DISCOVERY TODAY (2005)
Structure of the catalytic domain of human phosphodiesterase 5 with bound drug molecules
BJ Sung et al.
NATURE (2003)
Van der Waals radii of elements
SS Batsanov
INORGANIC MATERIALS (2001)