Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Dec 30, 2024
Date Accepted: Jun 20, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Deep learning for classifying cancer types using multi-omics data
ABSTRACT
Background:
Cancer is one of the leading causes of disease burden globally, and early and accurate diagnosis is crucial for effective treatment. This study presents a deep learning-based model designed to classify five common types of cancer in Saudi Arabia: Breast, Colorectal, Thyroid, Non-Hodgkin Lymphoma (NHL), and Corpus Uteri.
Objective:
To determine whether incorporating multi-omics data, including RNAseq, mutation, and methylation data, could enhance classification accuracy.
Methods:
Utilizing a stacking ensemble learning approach, our model integrates five well-established methods: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Random Forest (RF). The methodology involves two main stages: data pre-processing (including normalization and feature extraction) and ensemble stacking classification. We prepared the data before applying the stacking model.
Results:
The stacking ensemble model achieved 98% accuracy with multi-omics versus 96% using RNA-seq, suggesting that multi-omics data can be used for diagnosis in primary care settings. The models used in ensemble learning are among the most widely utilized in cancer classification research. Their prevalent use in prior studies underscores their effectiveness and flexibility, enhancing the performance of multi-omics data integration.
Conclusions:
This study highlights the importance of advanced machine learning techniques in improving cancer detection and prognosis accuracy. It contributes valuable insights by applying ensemble learning to integrate multi-omics data for more effective cancer classification. Clinical Trial: Not Applicable
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.