UNCCER: unified network for cancer classification and efficient representation using microarray data

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dc.contributor.authorYounis, Haseeben
dc.contributor.authorBrahmi, Imaneen
dc.contributor.authorByrne, Jonathanen
dc.contributor.authorMinghim, Rosaneen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-02-20T14:28:39Z
dc.date.available2025-02-20T14:28:39Z
dc.date.issued2024-12-26en
dc.description.abstractIn recent years, there has been a significant advance in the use of machine learning (ML) techniques to extract gene expression data from microarray databases, particularly in cancer-related research. There no unified method for classifying cancer microarray data, even after ML adoption. Due to the high dimensionality of microarray data, it is difficult to extract the relevant features and provide insights that can be helpful in identifying cancer types and stages. In this paper, we propose a Unified Network for Cancer Classification and Efficient Representation (UNCCER) using Deep Learning (DL) on cancer microarray data. To implement this methodology, we employed a microarray database (CuMiDa) that has 78 carefully curated datasets for different types of cancers. Our single model has the capability to learn the patterns, cluster instances into their corresponding classes, and classify the cancer. We also used the Uniform Manifold Approximation and Projection (UMAP) to visualise, in low dimension, the instance separation both on original data and transformed data by our methodology. Using the proposed methodology, we achieved average 94% average accuracy, precision, recall, F1 Score, and 91% G-Mean.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationYounis, H., Brahmi, H.I., Byrne, J. and Minghim, R. (2024) ‘UNCCER: unified network for cancer classification and efficient representation using microarray data’, 2024 18th International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan, 26-27 December 2024, pp. 1–6. https://doi.org/10.1109/ICOSST64562.2024.10871147en
dc.identifier.doihttps://doi.org/10.1109/ICOSST64562.2024.10871147en
dc.identifier.eissn979-8-3315-0869-2en
dc.identifier.endpage6en
dc.identifier.isbn979-8-3315-0870-8en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17087
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2024 18th International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan, 26-27 December, 2024en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6222/IE/SFI Centre for Research Training in Advanced Networks for Sustainable Societies/en
dc.rights© 2025, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectDeep learningen
dc.subjectManifoldsen
dc.subjectDatabasesen
dc.subjectData visualizationen
dc.subjectPredictive modelsen
dc.subjectFeature extractionen
dc.subjectData modelsen
dc.subjectData miningen
dc.subjectGene expressionen
dc.subjectCanceren
dc.titleUNCCER: unified network for cancer classification and efficient representation using microarray dataen
dc.typeConference itemen
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