MANet: a deep learning framework for multi-cancer microarray analysis, classification, and visualization

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dc.contributor.authorYounis, Haseeben
dc.contributor.authorAzeem, Muhammaden
dc.contributor.authorRonan, Isabelen
dc.contributor.authorMinghim, Rosaneen
dc.date.accessioned2025-02-20T13:54:10Z
dc.date.available2025-02-20T13:54:10Z
dc.date.issued2024en
dc.description.abstractMachine learning (ML) methods have been used much more frequently in recent years to extract gene expression data from microarray studies, especially in cancer research. Even after the continued interest in applying ML to scientific cancer research, there is still no universal approach for categorizing cancer microarray data. A system is needed that can detect and classify a normal profile and a cancer profile, specifying the type of cancer. Due to the variance and high dimensionality of microarray data, it is difficult to extract the relevant descriptors and provide insights that can be helpful in identifying cancer types and stages. In this paper, we proposed MANet: a methodology using cancer microarray data based on Deep Learning (DL) to classify 13 different types of cancers as well as normal profiles. To implement this methodology, we used a Curated Microarray Database (CuMiDa) that has 78 datasets for different types of cancers. Due to the diverse feature vectors for each dataset, we used Principal Component Analysis (PCA) for uniform feature engineering. 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 the instance separation on original data. Additionally, this UMAP visualises segregation done by our methodology in low dimensions. Using the proposed methodology, we achieved approximately 80% average accuracy, precision, recall, and F1 Score for 14 classes using a single modelen
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationYounis, H., Azeem, M., Ronan, I. and Minghim, R. (2024) ‘MANet: a deep learning framework for multi-cancer microarray analysis, classification, and visualization’, in 2024 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 9-10 December 2024, pp. 1–6. https://doi.org/10.1109/FIT63703.2024.10838450en
dc.identifier.doihttps://doi.org/10.1109/FIT63703.2024.10838450en
dc.identifier.eissn979-8-3315-1050-3en
dc.identifier.endpage6en
dc.identifier.isbn979-8-3315-1051-0en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17086
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2024 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 9-10 December, 2024en
dc.rights© 2024, 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.subjectAccuracyen
dc.subjectData visualizationen
dc.subjectBiologyen
dc.subjectVectorsen
dc.subjectReliabilityen
dc.subjectData miningen
dc.subjectCanceren
dc.subjectPrincipal component analysisen
dc.subjectCancer microarrayen
dc.subjectMicroarray cancer classificationen
dc.subjectTransfer learningen
dc.titleMANet: a deep learning framework for multi-cancer microarray analysis, classification, and visualizationen
dc.typeConference itemen
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