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Predicting Drug-Drug Interactions Through Similarity-Based Link Prediction Over Web Data

Achille Fokoue, Oktie Hassanzadeh, Mohammad Sadoghi, Ping Zhang
2016 Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion  
The link prediction is performed using a logistic regression model over several similarity matrices built using different drug similarity measures.  ...  The system first creates a knowledge graph out of input data sources through large-scale semantic integration, and then performs link prediction among drug entities in the graph through large-scale similarity  ...  APIs & Web Interface We provide the outcome of our similarity-based DDI prediction through a set of APIs.  ... 
doi:10.1145/2872518.2890532 dblp:conf/www/FokoueHSZ16 fatcat:gjc64c3255h3pojga7lcgpk5nm

Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

Abhik Seal, David J. Wild
2018 BMC Bioinformatics  
Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network.  ...  using permutation based testing.  ...  HeatS uses only the drug target bipartite data matrix and NBI uses similarity matrices of drug chemical similarity matrix and protein similarity matrix.  ... 
doi:10.1186/s12859-018-2254-7 pmid:30012095 pmcid:PMC6047136 fatcat:glm4lvmvtfbsfenl6xyemqmibm

Netpredictor: R and Shiny package to perform Drug-Target Bipartite network analysis and prediction of missing links [article]

ABHIK SEAL, David John Wild
2016 bioRxiv   pre-print
Netpredictor is an R package for prediction of missing links in any given bipartite network.  ...  using permutation based testing.  ...  Chen [20] and Seal [21] have used random walk with restart (RWR) based method to predict drug target interactions on a heterogeneous network made up of drug-drug similarity, protein-protein similarity  ... 
doi:10.1101/080036 fatcat:jetdv26h4fe7hhm5texv2k7vpi

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning

Andrej Kastrin, Polonca Ferk, Brane Leskošek, Jinn-Moon Yang
2018 PLoS ONE  
based on topological and semantic similarity features.  ...  OPEN ACCESS Citation: Kastrin A, Ferk P, Leskošek B (2018) Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning.  ...  Drug-drug interaction through molecular structure similarity analysis.  ... 
doi:10.1371/journal.pone.0196865 pmid:29738537 pmcid:PMC5940181 fatcat:tdmoglmtorhivpjjiuub7n2csy

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

Tiejun Cheng, Ming Hao, Takako Takeda, Stephen H. Bryant, Yanli Wang
2017 AAPS Journal  
The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs.  ...  It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data  ...  Most studies for DTI prediction were based on the hypothesis that similar targets interact with same drug, and the same target interacts with similar drugs.  ... 
doi:10.1208/s12248-017-0092-6 pmid:28577120 fatcat:7bfu7b7mbran7lkhjuwblsjgzq

PhLeGrA

Maulik R. Kamdar, Mark A. Musen
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs.  ...  To tackle these integrative challenges, the Semantic Web community has published and linked several datasets in the Life Sciences Linked Open Data (LSLOD) cloud using established W3C standards.  ...  Acknowledgments The authors acknowledge Michel Dumontier for helping use Bio2RDF linked data. This work is supported in part by Grant HG004028 from the US National Institutes of Health.  ... 
doi:10.1145/3038912.3052692 pmid:29479581 pmcid:PMC5824722 dblp:conf/www/KamdarM17 fatcat:y5ukjcapqvdtbdfbw52fscvs2a

Predicting Drug-Drug Interactions Through Large-Scale Similarity-Based Link Prediction [chapter]

Achille Fokoue, Mohammad Sadoghi, Oktie Hassanzadeh, Ping Zhang
2016 Lecture Notes in Computer Science  
As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions.  ...  The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs and among newly developed and existing drugs.  ...  Conclusion In this paper, we presented Tiresias, a computational framework that predicts DDIs through large-scale similarity-based link prediction.  ... 
doi:10.1007/978-3-319-34129-3_47 fatcat:qxr7buoflbakla32zeogdeuacy

Neuro-symbolic representation learning on biological knowledge graphs

Mona Alshahrani, Mohammad Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf, Janet Kelso
2017 Bioinformatics  
We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target  ...  Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries.  ...  Nevertheless, the embeddings generated for drugs based on the corpus generated by random walks can encode some of this information, for example by linking both genes and drugs to similar phenotypes (and  ... 
doi:10.1093/bioinformatics/btx275 pmid:28449114 pmcid:PMC5860058 fatcat:6henlhfbgvee3m74izxt4rgcmq

Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud

Maulik R Kamdar, Mark A Musen
2018 AMIA Annual Symposium Proceedings  
We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network.  ...  Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug-drug interactions (DDIs).  ...  The authors also acknowledge Michel Dumontier for his help using Bio2RDF linked data. This work is supported by Grant HG004028 from the US National Institutes of Health.  ... 
pmid:29854169 pmcid:PMC5977627 fatcat:lvr422wxana2hh5zkeyik67axa

Provenance-Centered Dataset of Drug-Drug Interactions [chapter]

Juan M. Banda, Tobias Kuhn, Nigam H. Shah, Michel Dumontier
2015 Lecture Notes in Computer Science  
In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide  ...  Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank  ...  This prediction method infers both pharmacodynamic and pharmacokinetic interactions based on their similarity to existing known adverse events that result from a common metabolizing enzyme (CYP).  ... 
doi:10.1007/978-3-319-25010-6_18 fatcat:fln4bwsyd5akpfqxj3hgaeexze

Provenance-Centered Dataset of Drug-Drug Interactions [article]

Juan M. Banda and Tobias Kuhn and Nigam H. Shah and Michel Dumontier
2015 arXiv   pre-print
In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide  ...  Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank  ...  This prediction method infers both pharmacodynamic and pharmacokinetic interactions based on their similarity to existing known adverse events that result from a common metabolizing enzyme (CYP).  ... 
arXiv:1507.05408v1 fatcat:czlrxdgvffewnpjojtealjag3i

Open chemoinformatic resources to explore the structure, properties and chemical space of molecules

Mariana González-Medina, J. Jesús Naveja, Norberto Sánchez-Cruz, José L. Medina-Franco
2017 RSC Advances  
The next section focuses on web-based application to predict ADME and toxicity properties, which are essential in drug discovery programs.  ...  molecules, analog based drug design, and similarity with known anticancer molecules CancerIN was built using python scripts 45 CDRUG CDRUG is a web server for predicting anticancer efficacy  ... 
doi:10.1039/c7ra11831g fatcat:jxncc4n6zzgltjzeus5cswh4sy

systemsDock: a web server for network pharmacology-based prediction and analysis

Kun-Yi Hsin, Yukiko Matsuoka, Yoshiyuki Asai, Kyota Kamiyoshi, Tokiko Watanabe, Yoshihiro Kawaoka, Hiroaki Kitano
2016 Nucleic Acids Research  
We present systemsDock, a web server for network pharmacology-based prediction and analysis, which permits docking simulation and molecular pathway map for comprehensive characterization of ligand selectivity  ...  drug candidate.  ...  It is only relatively recently that interactive web interfaces for prediction of drug-target interactions have become available to non-commercial research groups.  ... 
doi:10.1093/nar/gkw335 pmid:27131384 pmcid:PMC4987901 fatcat:tibcoxqsobdfdjf7jriiyixwju

Mining Drug Properties for Decision Support in Dental Clinics [chapter]

Wee Pheng Goh, Xiaohui Tao, Ji Zhang, Jianming Yong
2017 Lecture Notes in Computer Science  
Although there are many methods on extracting information on drug interactions, they do not integrate with the patients' medical history.  ...  Since a drug is to be avoided if it is similar to a drug that patient is allergic to, our model will help dentist decide if a drug is suitable for prescription to the patient.  ...  Based on a novel data-driven text mining technique, clusters of drugs which have adverse interactions are collected, together with their properties, by identifying their field markers from the web content  ... 
doi:10.1007/978-3-319-57529-2_30 fatcat:gu3rhc552jdpnpwgxlidjqmunq

Drug Repositioning Network System Using the Power of Network Analysis and Machine Learning to Predict new Indications for the Approved Drugs "Drug Repositioning and Rate the Level of Drug Similarity

Sherief El Rweney
2018 Journal of Proteomics & Bioinformatics  
The drug repositioning network system goes through the similarity of drugs based on the percentage of shard drugs exist on each prediction result to calculate the success rate of the drug repositioning  ...  Web based system with search engines to be published to the public to be able to query intensive data related to drug repositioning and similarity to help researchers on their work.  ... 
doi:10.4172/jpb.1000463 fatcat:qqeo66xkkjfm7bsmeznq7uf4te
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