Deep learning-based explainable approaches for RNA-seq gene expression data analysis

Shamima Nasrin; Md Zahangir Alom; Simon Khan; Tarek M. Taha

doi:10.1117/12.3030851

3 October 2024 Deep learning-based explainable approaches for RNA-seq gene expression data analysis

Shamima Nasrin, Md Zahangir Alom, Simon Khan, Tarek M. Taha

Author Affiliations +

Proceedings Volume 13138, Applications of Machine Learning 2024; 1313804 (2024) https://doi.org/10.1117/12.3030851
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Explainable Machine Learning (XML) approaches are crucial for medical information processing tasks, particularly for multi-omics data analytics. The XML system not only provides better performance but also explains the inside of the finding better. Here, we proposed an end-to-end explainable system for analyzing high dimensional RNA-seq data using an unsupervised gene selection approach and supervised methods, including Deep Neural Network (DNN), Deep Convolutional Neural Network (DCNN), Support Vector Machine (SVM), and Random Forest (RF). The proposed approaches evaluate with publicly available datasets for five different cancers and Kawasaki disease (KD) classification. The deep learning-based approaches yield the 99.62% and 99. 25% average testing accuracy for cancer and KD classification tasks. Additionally, we introduce an explainable system that demonstrates the ability to select cancer and disease-specific gene sets, which could be used for further analysis to discover the biological inside of the cancers and KD diseases.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shamima Nasrin, Md Zahangir Alom, Simon Khan, and Tarek M. Taha "Deep learning-based explainable approaches for RNA-seq gene expression data analysis", Proc. SPIE 13138, Applications of Machine Learning 2024, 1313804 (3 October 2024); https://doi.org/10.1117/12.3030851

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