scholarly journals Drug target prediction using adverse event report systems: a pharmacogenomic approach

2012 ◽  
Vol 28 (18) ◽  
pp. i611-i618 ◽  
Author(s):  
M. Takarabe ◽  
M. Kotera ◽  
Y. Nishimura ◽  
S. Goto ◽  
Y. Yamanishi
Keyword(s):  
Adverse Event ◽  
Drug Target ◽  
Target Prediction ◽  
Adverse Event Report ◽  
Event Report ◽  
Drug Target Prediction

Risk of muscle toxicity events for daptomycin with and without statins: Analysis of the Japanese Adverse Event Report database

2021 ◽  
Author(s):  
Tomoyuki Yamada ◽  
Satoru Mitsuboshi ◽  
Kaoru Suzuki ◽  
Masami Nishihara ◽  
Kazuhisa Uchiyama
Keyword(s):  
Adverse Event ◽  
Adverse Event Report ◽  
Event Report ◽  
Muscle Toxicity

Drug–target prediction utilizing heterogeneous bio-linked network embeddings

2019 ◽  
Author(s):  
Nansu Zong ◽  
Rachael Sze Nga Wong ◽  
Yue Yu ◽  
Andrew Wen ◽  
Ming Huang ◽  
...  
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Machine Learning Algorithms ◽  
Association Mining ◽  
Drug Target Prediction ◽  
Specific Prediction ◽  
Series Of Experiments ◽  
Inference Methods ◽  
Novel Drug ◽  
Prediction Strategy

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

scholarly journals Drug Target Prediction Based on the Herbs Components: The Study on the Multitargets Pharmacological Mechanism of Qishenkeli Acting on the Coronary Heart Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Wang ◽  
Zhongyang Liu ◽  
Chun Li ◽  
Dong Li ◽  
Yulin Ouyang ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Angiotensin Ii ◽  
Drug Target ◽  
Drug Targets ◽  
Target Prediction ◽  
Coronary Artery Ligation ◽  
Potential Drug ◽  
Drug Target Prediction ◽  
Potential Drug Targets

In this paper, we present a case study of Qishenkeli (QSKL) to research TCM’s underlying molecular mechanism, based on drug target prediction and analyses of TCM chemical components and following experimental validation. First, after determining the compositive compounds of QSKL, we use drugCIPHER-CS to predict their potential drug targets. These potential targets are significantly enriched with known cardiovascular disease-related drug targets. Then we find these potential drug targets are significantly enriched in the biological processes of neuroactive ligand-receptor interaction, aminoacyl-tRNA biosynthesis, calcium signaling pathway, glycine, serine and threonine metabolism, and renin-angiotensin system (RAAS), and so on. Then, animal model of coronary heart disease (CHD) induced by left anterior descending coronary artery ligation is applied to validate predicted pathway. RAAS pathway is selected as an example, and the results show that QSKL has effect on both rennin and angiotensin II receptor (AT1R), which eventually down regulates the angiotensin II (AngII). Bioinformatics combing with experiment verification can provide a credible and objective method to understand the complicated multitargets mechanism for Chinese herbal formula.

Biochemical network-based drug-target prediction

2010 ◽  
Vol 21 (4) ◽  
pp. 511-516 ◽  
Author(s):  
Edda Klipp ◽  
Rebecca C Wade ◽  
Ursula Kummer
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Biochemical Network ◽  
Drug Target Prediction

scholarly journals Industry-scale application and evaluation of deep learning for drug target prediction

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Noé Sturm ◽  
Andreas Mayr ◽  
Thanh Le Van ◽  
Vladimir Chupakhin ◽  
Hugo Ceulemans ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Target Prediction ◽  
Drug Target Prediction
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