Drug-target interaction prediction through domain-tuned network-based inference. Alaimo, S., Pulvirenti, A., Giugno, R., & Ferro, A. Bioinformatics, 29(16):2004--2008, Aug, 2013. doi abstract bibtex MOTIVATION: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain. RESULTS: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs. AVAILABILITY: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.
@article{Alaimo:2013ev,
Abstract = {MOTIVATION: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.
RESULTS: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.
AVAILABILITY: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.},
Author = {Alaimo, Salvatore and Pulvirenti, Alfredo and Giugno, Rosalba and Ferro, Alfredo},
Date-Added = {2015-03-04 15:28:41 +0000},
Date-Modified = {2015-03-04 15:28:41 +0000},
Doi = {10.1093/bioinformatics/btt307},
Journal = {Bioinformatics},
Journal-Full = {Bioinformatics (Oxford, England)},
Mesh = {Algorithms; Databases, Pharmaceutical; Drug Discovery; Protein Structure, Tertiary; Proteins},
Month = {Aug},
Number = {16},
Pages = {2004--2008},
Pmc = {PMC3722516},
Pmid = {23720490},
Pst = {ppublish},
Title = {Drug-target interaction prediction through domain-tuned network-based inference},
Volume = {29},
Year = {2013},
Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btt307}}
Downloads: 0
{"_id":"uu9GL3nNzDkGDCMAr","bibbaseid":"alaimo-pulvirenti-giugno-ferro-drugtargetinteractionpredictionthroughdomaintunednetworkbasedinference-2013","downloads":0,"creationDate":"2016-02-18T13:03:27.917Z","title":"Drug-target interaction prediction through domain-tuned network-based inference","author_short":["Alaimo, S.","Pulvirenti, A.","Giugno, R.","Ferro, A."],"year":2013,"bibtype":"article","biburl":"https://dl.dropboxusercontent.com/u/26998770/main.bib","bibdata":{"bibtype":"article","type":"article","abstract":"MOTIVATION: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain. RESULTS: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs. AVAILABILITY: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.","author":[{"propositions":[],"lastnames":["Alaimo"],"firstnames":["Salvatore"],"suffixes":[]},{"propositions":[],"lastnames":["Pulvirenti"],"firstnames":["Alfredo"],"suffixes":[]},{"propositions":[],"lastnames":["Giugno"],"firstnames":["Rosalba"],"suffixes":[]},{"propositions":[],"lastnames":["Ferro"],"firstnames":["Alfredo"],"suffixes":[]}],"date-added":"2015-03-04 15:28:41 +0000","date-modified":"2015-03-04 15:28:41 +0000","doi":"10.1093/bioinformatics/btt307","journal":"Bioinformatics","journal-full":"Bioinformatics (Oxford, England)","mesh":"Algorithms; Databases, Pharmaceutical; Drug Discovery; Protein Structure, Tertiary; Proteins","month":"Aug","number":"16","pages":"2004--2008","pmc":"PMC3722516","pmid":"23720490","pst":"ppublish","title":"Drug-target interaction prediction through domain-tuned network-based inference","volume":"29","year":"2013","bdsk-url-1":"http://dx.doi.org/10.1093/bioinformatics/btt307","bibtex":"@article{Alaimo:2013ev,\n\tAbstract = {MOTIVATION: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.\nRESULTS: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.\nAVAILABILITY: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.},\n\tAuthor = {Alaimo, Salvatore and Pulvirenti, Alfredo and Giugno, Rosalba and Ferro, Alfredo},\n\tDate-Added = {2015-03-04 15:28:41 +0000},\n\tDate-Modified = {2015-03-04 15:28:41 +0000},\n\tDoi = {10.1093/bioinformatics/btt307},\n\tJournal = {Bioinformatics},\n\tJournal-Full = {Bioinformatics (Oxford, England)},\n\tMesh = {Algorithms; Databases, Pharmaceutical; Drug Discovery; Protein Structure, Tertiary; Proteins},\n\tMonth = {Aug},\n\tNumber = {16},\n\tPages = {2004--2008},\n\tPmc = {PMC3722516},\n\tPmid = {23720490},\n\tPst = {ppublish},\n\tTitle = {Drug-target interaction prediction through domain-tuned network-based inference},\n\tVolume = {29},\n\tYear = {2013},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btt307}}\n\n","author_short":["Alaimo, S.","Pulvirenti, A.","Giugno, R.","Ferro, A."],"key":"Alaimo:2013ev","id":"Alaimo:2013ev","bibbaseid":"alaimo-pulvirenti-giugno-ferro-drugtargetinteractionpredictionthroughdomaintunednetworkbasedinference-2013","role":"author","urls":{},"downloads":0},"search_terms":["drug","target","interaction","prediction","through","domain","tuned","network","based","inference","alaimo","pulvirenti","giugno","ferro"],"keywords":[],"authorIDs":[],"dataSources":["c5japf9eAQRaeMS4h"]}