scholarly journals Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-Ying Yan ◽  
Peng-Wei Yin ◽  
Xiao-Meng Wu ◽  
Jia-Xin Han

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

2016 ◽  
Vol 12 (1) ◽  
pp. 70 ◽  
Author(s):  
Farshad Shams ◽  
Paolo Capodieci ◽  
Antonio Cerone ◽  
Romano Fantacci ◽  
Dania Marabissi ◽  
...  

2020 ◽  
Vol 67 (1) ◽  
pp. 48-59
Author(s):  
Daniel S. Sarasin ◽  
Jason W. Brady ◽  
Roy L. Stevens

For decades, the dental profession has provided the full spectrum of anesthesia services ranging from local anesthesia to general anesthesia in the office-based ambulatory environment to alleviate pain and anxiety. However, despite a reported record of safety, complications occasionally occur. Two common contributing factors to general anesthesia and sedation complications are medication errors and adverse drug events. The prevention and early detection of these complications should be of paramount importance to all dental providers who administer or otherwise use anesthesia services. Unfortunately, there is a lack of literature currently available regarding medication errors and adverse drug events involving anesthesia for dentistry. As a result, the profession is forced to look to the medical literature regarding these issues not only to assess the likely severity of the problem but also to develop preventive methods specific for general anesthesia and sedation as practiced within dentistry. Part 1 of this 2-part article illuminated the problems of medication errors and adverse drug events, primarily as documented within medicine. Part 2 will focus on how these complications affect dentistry, discuss several of the methods that medical anesthesia has implemented to manage such problems that may have utility in dentistry, and introduce a novel method for addressing these issues within dentistry known as the Dental Anesthesia Medication Safety Paradigm (DAMSP).


Author(s):  
Sergio Greco ◽  
Ester Zumpano

Data integration aims at providing a uniform integrated access to multiple heterogeneous information sources, which were designed independently for autonomous applications and whose contents are strictly related.


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