A Unified Approach to Classifying Subtypes of All Cancers Using an Adaptive Learning Model with Focal Loss 


Vol. 14,  No. 11, pp. 889-895, Nov.  2025
https://doi.org/10.3745/TKIPS.2025.14.11.889


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  Abstract

Cancer subtyping is critical because treatment responses and side effects vary by subtype. We developed an adaptive neural network with focal loss (ANetFL) that classifies cancer subtypes across multiple cancer types using gene expression data. ANetFL automatically adjusts the learning rate, manages class imbalance by addressing minority and hard-to-classify subtypes, and identifies key genes characterizing cancer subtypes. In independent testing on 15 cancer types, ANetFL consistently achieved high accuracy across all metrics and outperformed existing methods. These findings suggest that ANetFL and the discovered key genes can support precise cancer subtype classification, facilitating the selection of more effective and personalized therapeutic strategies.

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[IEEE Style]

G. Deng, S. Wang, K. Han, "A Unified Approach to Classifying Subtypes of All Cancers Using an Adaptive Learning Model with Focal Loss," The Transactions of the Korea Information Processing Society, vol. 14, no. 11, pp. 889-895, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.889.

[ACM Style]

Guiyuan Deng, Shiyang Wang, and Kyungsook Han. 2025. A Unified Approach to Classifying Subtypes of All Cancers Using an Adaptive Learning Model with Focal Loss. The Transactions of the Korea Information Processing Society, 14, 11, (2025), 889-895. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.889.

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