Filters








183,488 Hits in 3.7 sec

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  
The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs.  ...  As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions.  ...  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

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 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  ...  The link prediction is performed using a logistic regression model over several similarity matrices built using different drug similarity measures.  ...  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

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  
We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides.  ...  based on topological and semantic similarity features.  ...  Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions.  ... 
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.  ...  essential for large-scale and automated information integration.  ...  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

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction [article]

Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
2020 arXiv   pre-print
Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community.  ...  This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view  ...  Many baseline approaches utilizing similarity-based fingerprints need plenty of non-structural similarity features, which may be absent in this large scale dataset.  ... 
arXiv:2010.11711v2 fatcat:ryzrp7rosnblhc3bpcgnrmvxp4

DLS: A Link Prediction Method Based on Network Local Structure for Predicting Drug-Protein Interactions

Wei Wang, Hehe Lv, Yuan Zhao, Dong Liu, Yongqing Wang, Yu Zhang
2020 Frontiers in Bioengineering and Biotechnology  
We propose a link prediction method based on drug-protein interaction (DPI) local structural similarity (DLS) for predicting the DPIs.  ...  The studies on drug-protein interactions (DPIs) had significant for drug repositioning, drug discovery, and clinical medicine.  ...  For example, i and j interact indirectly through the drug i-protein-drug-protein j, then i and j interact through the secondary pathway.  ... 
doi:10.3389/fbioe.2020.00330 pmid:32391341 pmcid:PMC7193019 fatcat:iv2rrvg5wraqlnhrrseuj7axli

Providing data science support for systems pharmacology and its implications to drug discovery

Thomas Hart, Lei Xie
2016 Expert Opinion on Drug Discovery  
deconvolution and personalized adverse drug reaction prediction.  ...  The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from  ...  The similarity-based method is on center stage of drug-target prediction.  ... 
doi:10.1517/17460441.2016.1135126 pmid:26689499 pmcid:PMC4988863 fatcat:ol5ra4b2efewtiietrqhcocqle

A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions

Bipin Nair B.J, Lijo Joy
2018 International Journal of Engineering & Technology  
finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.  ...  we are predicting the hot region using prediction algorithm.  ...  We are collecting the large-scale data of drug from drug bank and the fingerprinting are used to predict the similarity between drugs.  ... 
doi:10.14419/ijet.v7i1.9.9752 fatcat:lh34szujqvecpokepx5kw7b774

Identification of drug candidates and repurposing opportunities through compound–target interaction networks

Anna Cichonska, Juho Rousu, Tero Aittokallio
2015 Expert Opinion on Drug Discovery  
Similarity-based machine learning methods for predicting drug--target interactions: a brief review. Brief Bioinform 2014;15(5):734-47 .  ...  [26] are illustrative examples of large-scale binding and functional assays, respectively, both generating kinome-scale compound--target interaction maps.  ...  One of the very first papers that discussed the concept of network pharmacology and its potential in drug discovery. 11. Tang J, Aittokallio T.  ... 
doi:10.1517/17460441.2015.1096926 pmid:26429153 fatcat:vtz37pji6jcnlmcuw6k3v7t3kq

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures [article]

Guy Shtar, Lior Rokach, Bracha Shapira
2019 arXiv   pre-print
We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods for drug-drug interaction prediction based on artificial neural networks and factor propagation  ...  Computational techniques can be used to predict potential drug-drug interactions.  ...  Our methods can be used on a large-scale and applied for link prediction problems in domains other than drug-drug interaction prediction.  ... 
arXiv:1903.04571v1 fatcat:wp3yyix2lbhzpbgkkxpg4rgwga

Drug-target and disease networks: polypharmacology in the post-genomic era

Ali Masoudi-Nejad, Zaynab Mousavian, Joseph H Bozorgmehr
2013 In Silico Pharmacology  
Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction  ...  An important notion that has emerged in postgenomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of  ...  To predict the drug-target interaction, another interesting approach was proposed by Campillos et al. based on the side-effect similarities between known drugs (Campillos et al. 2008 ).  ... 
doi:10.1186/2193-9616-1-17 pmid:25505661 pmcid:PMC4230718 fatcat:daaturwzxzdsdlkkgefqwsjrom

A Survey on Drug-Target Interaction Prediction Methods Analysis of Prediction Mechanisms for Drug Target Discovery

Shyama M Nair
2018 International Journal for Research in Applied Science and Engineering Technology  
In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of drug and protein data.  ...  Prediction of drug-target interactions can be done by experimentally and it is very expensive and time consuming.  ...  MFDR is able to predict large-scale drug-target interactions with high accuracy and achieves results better than other feature-based approaches.Limin Li et al.  ... 
doi:10.22214/ijraset.2018.3057 fatcat:s3qchrwn5fbv3e3bkt7nfjtaum

Evaluation of Knowledge Graph Embedding Approaches for Drug-Drug Interaction Prediction using Linked Open Data

Remzi Celebi, Erkan Yasar, Huseyin Uyar, Ozgur Gumus, Oguz Dikenelli, Michel Dumontier
2018 Figshare  
Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process.  ...  In this work, we have applied Knowledge Graph (KG) Embedding approaches to extract feature vector representation of drugs using LOD to predict potential drug-drug interactions.  ...  This work was also supported by Ege University Research Fund through the 16-MUH-095 BAP project.  ... 
doi:10.6084/m9.figshare.7325186 fatcat:mddmxn3gnngybmycbr7yi7tgsu

Evaluation of Knowledge Graph Embedding Approaches for Drug-Drug Interaction Prediction using Linked Open Data

Remzi Celebi, Erkan Yasar, Huseyin Uyar, Ozgur Gumus, Oguz Dikenelli, Michel Dumontier
2018 Figshare  
Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process.  ...  In this work, we have applied Knowledge Graph (KG) Embedding approaches to extract feature vector representation of drugs using LOD to predict potential drug-drug interactions.  ...  This work was also supported by Ege University Research Fund through the 16-MUH-095 BAP project.  ... 
doi:10.6084/m9.figshare.7325186.v1 fatcat:mw4yogl4tjcz3ppg2n5fuq5gjy

Drug repositioning by integrating target information through a heterogeneous network model

Wenhui Wang, Sen Yang, Xiang Zhang, Jing Li
2014 Computer applications in the biosciences : CABIOS  
Results: In this article, we have proposed a computational framework based on a heterogeneous network model and applied the approach on drug repositioning by using existing omics data about diseases, drugs  ...  The novelty of the framework lies in the fact that the strength between a disease-drug pair is calculated through an iterative algorithm on the heterogeneous graph that also incorporates drug-target information  ...  The evaluation is mainly based on leave-one-out cross-validation (LOOCV) experiments on large-scale omics data from existing databases [e.g. DrugBank, OMIM (Hamosh et al., 2005) ].  ... 
doi:10.1093/bioinformatics/btu403 pmid:24974205 pmcid:PMC4184255 fatcat:j55fwsfjr5c6bazniyzxc6kqge
« Previous Showing results 1 — 15 out of 183,488 results