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dc.contributor.authorTimilsina, Mohan
dc.contributor.authorMc Kernan, Declan Patrick
dc.contributor.authorYang, Haixuan
dc.contributor.authord’Aquin, Mathieu
dc.date.accessioned2021-01-11T09:58:21Z
dc.date.available2021-01-11T09:58:21Z
dc.date.issued2020-10-16
dc.identifier.citationTimilsina, Mohan, Mc Kernan, Declan Patrick, Yang, Haixuan, & d’Aquin, Mathieu. (2020). Synergy between embedding and protein functional association networks for drug label prediction using harmonic function. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). doi:10.1109/TCBB.2020.3031696en_IE
dc.identifier.issn1557-9964
dc.identifier.urihttp://hdl.handle.net/10379/16447
dc.description.abstractSemi-Supervised Learning (SSL) is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage of unlabeled data. For instance, to identify drugs and targets where a few genes are known to be associated with a specific target for drugs and considered as labeled data. Labeling the genes requires laboratory verification and validation. This process is usually very time consuming and expensive. Thus, it is useful to estimate the functional role of drugs from unlabeled data using computational methods. To develop such a model, we used openly available data resources to create (i) drugs and genes, (ii) genes and disease, bipartite graphs. We constructed the genetic embedding graph from the two bipartite graphs using Tensor Factorization methods. We integrated the genetic embedding graph with the publicly available genetic interaction graphs. Our results show the usefulness of the integration by effectively predicting drug labels.en_IE
dc.description.sponsorshipWe would like to thank the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P for the financial support to conduct this research.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherACM and IEEEen_IE
dc.relation.ispartofIEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACMen
dc.subjectLabel Propagationen_IE
dc.subjectNetworksen_IE
dc.subjectPredictionen_IE
dc.subjectEmbeddingsen_IE
dc.subjectHarmonicen_IE
dc.titleSynergy between embedding and protein functional association networks for drug label prediction using harmonic functionen_IE
dc.typeArticleen_IE
dc.date.updated2021-01-10T12:23:07Z
dc.identifier.doi10.1109/TCBB.2020.3031696
dc.local.publishedsourcehttps://dx.doi.org/10.1109/TCBB.2020.3031696en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.internal.rssid24309514
dc.local.contactMohan Timilsina, Data Science Institute, Nui Galway. Email: m.timilsina1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE
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