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The SIDER database of drugs and side effects

Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, Peer Bork
2015 Nucleic Acids Research  
To this end, we have created the SIDER ('Side Effect Resource', database of drugs and ADRs.  ...  Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs.  ...  We present here a new release of the SIDER database with over 40% more drugs, ADRs and drug-ADR pairs compared to the previous version and more than 2-fold as many drug-ADR pairs as the published version  ... 
doi:10.1093/nar/gkv1075 pmid:26481350 pmcid:PMC4702794 fatcat:4vassewagbbvvdsr2gkygugzg4

Investigating Side Effect Modules in the Interactome and Their Use in Drug Adverse Effect Discovery [chapter]

Emre Guney
2017 Complex Networks VIII  
We find that drug targets are closer in the interactome to the proteins inducing the known side effects of the drug compared to the proteins associated with the rest of the side effects.  ...  In this study, we investigate the role of network topology in explaining observed side effects of drugs.  ...  Acknowledgements The author is grateful to Dr. Patrick Aloy for providing computational resources for this study and the members of the lab for fruitful discussions.  ... 
doi:10.1007/978-3-319-54241-6_21 fatcat:l25vqid43bba3cawvbklkvl7fi

Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams

Ryan Eshleman, Rahul Singh
2016 BMC Bioinformatics  
Methods: We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices.  ...  The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users.  ...  Availability of data and material The data files to construct DEG Sider are available through the SIDER website:  ... 
doi:10.1186/s12859-016-1220-5 pmid:27766937 pmcid:PMC5073861 fatcat:dg4ym374kjeznkuo63ycftagty

Investigating side effect modules in the interactome and their use in drug adverse effect discovery [article]

Emre Guney
2016 bioRxiv   pre-print
We find that drug targets are closer in the interactome to the proteins inducing the known side effects of the drug compared to the proteins associated with the rest of the side effects.  ...  In this study, we investigate the role of network topology in explaining observed side effects of drugs.  ...  Acknowledgements The author is grateful to Dr. Patrick Aloy for providing computational resources for this study and the members of the lab for fruitful discussions.  ... 
doi:10.1101/089730 fatcat:3dmlc5qixrg2pelmm6upqea3vi

Network-based method to infer the contributions of proteins to the etiology of drug side effects

Rui Li, Ting Chen, Shao Li
2015 Quantitative Biology  
We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database.  ...  Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development.  ...  (SIDER2) versions of SIDER, a database recording drug-side effect relationships.  ... 
doi:10.1007/s40484-015-0051-0 fatcat:6yqvednb5jeo5pmhjfabpx3y3m

Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature

Rong Xu, QuanQiu Wang
2015 BMC Bioinformatics  
Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discovery and drug repositioning.  ...  Results: On average, the KD approach has achieved a precision of 0.335, a recall of 0.509, and an F1 of 0.392, which is significantly better than a SVM-based machine learning approach (precision: 0.135  ...  The overall performance depends on the accuracy and comprehensiveness of the SIDER database.  ... 
doi:10.1186/1471-2105-16-s5-s6 pmid:25860223 pmcid:PMC4402591 fatcat:7jhupjq57jhz5ial7js5rvgmbi

GraphSAW: A web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data

Alban Shoshi, Tobias Hoppe, Benjamin Kormeier, Venus Ogultarhan, Ralf Hofestädt
2015 BMC Medical Informatics and Decision Making  
The concordance of drug interactions was about 12% and 9% of drug side effects.  ...  The results of the data analysis give an overview of all theoretically possible drug interactions and side effects. The evaluation shows a mismatch between pharmaceutical and molecular databases.  ...  The project was funded by the Federal Ministry of Economics and Technology (BMWi) and European Social Fund (ESF).  ... 
doi:10.1186/s12911-015-0139-5 pmid:25881043 pmcid:PMC4350865 fatcat:injitezth5fetdeptjulot7sxe

An ensemble approach for drug side effect prediction

Md Jamiul Jahid, Jianhua Ruan
2013 2013 IEEE International Conference on Bioinformatics and Biomedicine  
We applied our approach to 1385 side-effects in the SIDER database for 888 drugs. Results show that our approach outperformed previously published approaches and standard classifiers.  ...  Furthermore, we applied our method to a number of uncharacterized drug molecules in DrugBank database and predict their side-effect profiles for future usage.  ...  This research was supported in part by the National Science Foundation (1IS-1218201), National Institutes of Health (SC3GM086305, U54CAl13001, G12MD007591 (Computational Systems Biology Core)), and a UTSA  ... 
doi:10.1109/bibm.2013.6732532 pmid:25327524 pmcid:PMC4197807 fatcat:26dltwwqabfzfjg5yhqiq6cjxy

Targets of drugs are generally and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Áron R. Perez-Lopez, Kristóf Z. Szalay, Dénes Türei, Dezső Módos, Katalin Lenti, Tamás Korcsmáros, Peter Csermely
2015 Scientific Reports  
Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those  ...  Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations  ...  T.K. was a grantee of the János Bolyai Scholarship of the Hungarian Academy of Sciences, and is supported by a fellowship in computational biology at The Genome Analysis Centre, in partnership with the  ... 
doi:10.1038/srep10182 pmid:25960144 pmcid:PMC4426692 fatcat:jgzn2aalrbdlzeylumbucmb7m4

Data-Driven Prediction of Drug Effects and Interactions

N. P. Tatonetti, P. P. Ye, R. Daneshjou, R. B. Altman
2012 Science Translational Medicine  
We also present a comprehensive database of drug effects (OFFSIDES) and a database of drug-drug interaction side effects (TWOSIDES).  ...  We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions  ...  Oliver, and D. Ludwig for useful comments and discussion.  ... 
doi:10.1126/scitranslmed.3003377 pmid:22422992 pmcid:PMC3382018 fatcat:mp3qniumxnhqnlh7wcyfearjum

Integrative relational machine-learning for understanding drug side-effect profiles

Emmanuel Bresso, Renaud Grisoni, Gino Marchetti, Arnaud Karaboga, M Souchet, Marie-Dominique Devignes, Malika Smaïl-Tabbone
2013 BMC Bioinformatics  
Results: In this work, drug annotations are collected from SIDER and DrugBank databases.  ...  Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs.  ...  Thanks to Dave Ritchie and Anisah Ghoorah for their careful reading of the paper. Author details  ... 
doi:10.1186/1471-2105-14-207 pmid:23802887 pmcid:PMC3710241 fatcat:ieydyhshfjfspmofazziubge5m

Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data [article]

Heeyoung Kwak, Minwoo Lee, Seunghyun Yoon, Jooyoung Chang, Sangmin Park, Kyomin Jung
2020 arXiv   pre-print
We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database.  ...  The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships.  ...  registered in the SIDER database respectively, and E SIDER is the set of drug-disease pairs that are known to have side effect relation according to the SIDER database.  ... 
arXiv:2004.00407v1 fatcat:on4dvs2n7zen5jh3jk7xooo3pq

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

M. Takarabe, M. Kotera, Y. Nishimura, S. Goto, Y. Yamanishi
2012 Bioinformatics  
drug-target interactions on a large scale from the integration of pharmacological similarity of drugs and genomic sequence similarity of target proteins in the framework of a pharmacogenomic approach.  ...  The proposed method was applicable to a large number of drugs and it was useful especially for predicting unknown drug-target interactions that could not be expected from drug chemical structures.  ...  Side effect keywords for drugs were obtained from the SIDER database [] which contains information about marketed medicines and their recorded side effects or adverse drug reactions  ... 
doi:10.1093/bioinformatics/bts413 pmid:22962489 pmcid:PMC3436840 fatcat:rpqkm3xpvzeofp7bnvrciymwwa

Using Drug Similarities for Discovery of Possible Adverse Reactions

Emir Muñoz, Vít Nováček, Pierre-Yves Vandenbussche
2017 AMIA Annual Symposium Proceedings  
We implemented the proposed method in the form of a software prototype and evaluated our approach by discarding known drug-side effect links from our data and checking whether our prototype is able to  ...  side effects.  ...  The materials contain pre-processed versions of DrugBank and SIDER data sets from Bio2RDF v4.0, including fixing of a few syntax errors and a simple documentation.  ... 
pmid:28269889 pmcid:PMC5333276 fatcat:lhteztt2wjcodiyhrtvlgw3ul4

Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)

Zhichao Liu, Qiang Shi, Don Ding, Reagan Kelly, Hong Fang, Weida Tong, Greg Tucker-Kellogg
2011 PLoS Computational Biology  
We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase.  ...  We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential.  ...  We used the SIDER database [12] to identify drugs and associated side effects.  ... 
doi:10.1371/journal.pcbi.1002310 pmid:22194678 pmcid:PMC3240589 fatcat:hrz7snb57vetxen47y5kheamci
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