<p>An autoencoder and generative adversarial networks approach for multi-omics data imbalanced class handling and classification</p>

Ibrahim Al-Hurani; Abedalrhman Alkhateeb; Salama  Ikki

doi:10.7490/f1000research.1120071.1

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Al-Hurani I, Alkhateeb A and Ikki S. An autoencoder and generative adversarial networks approach for multi-omics data imbalanced class handling and classification [version 1; not peer reviewed]. F1000Research 2025, 14:2 (poster) (https://doi.org/10.7490/f1000research.1120071.1)

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An autoencoder and generative adversarial networks approach for multi-omics data imbalanced class handling and classification

Ibrahim Al-Hurani¹, Abedalrhman Alkhateeb, Salama Ikki

Published 01 Jan 2025 (https://doi.org/10.7490/f1000research.1120071.1)

Author Affiliations

¹ Electrical and Computer Engine, Lakehead University Faculty of Engineering, Thunder Bay, Ontario, Canada

Copyright © 2025 Al-Hurani I et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 01 Jan 2025

An autoencoder and generative adversarial networks approach for multi-omics data imbalanced class handling and classification
[version 1; not peer reviewed]

Ibrahim Al-Hurani¹, Abedalrhman Alkhateeb, Salama Ikki

Author Affiliations

¹ Electrical and Computer Engine, Lakehead University Faculty of Engineering, Thunder Bay, Ontario, Canada

Presented at

Intelligent Systems for Molecular Biology Conference (ISMB), 2025

Abstract

Competing Interests

No competing interests were disclosed

Keywords

autoencoder, generative adversarial networks, multi-omics, imbalanced

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