A Unified Approach to Classifying Subtypes of All Cancers Using an Adaptive Learning Model with Focal Loss
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Cancer subtyping is critical because treatment responses and side effects vary by subtype. We developed an adaptive neural networkwith focal loss (ANetFL) that classifies cancer subtypes across multiple cancer types using gene expression data. ANetFL automaticallyadjusts the learning rate, manages class imbalance by addressing minority and hard-to-classify subtypes, and identifies key genescharacterizing cancer subtypes. In independent testing on 15 cancer types, ANetFL consistently achieved high accuracy across all metricsand outperformed existing methods. These findings suggest that ANetFL and the discovered key genes can support precise cancer subtypeclassification, facilitating the selection of more effective and personalized therapeutic strategies.
Abstract
Cancer subtyping is critical because treatment responses and side effects vary by subtype. We developed an adaptive neural networkwith focal loss (ANetFL) that classifies cancer subtypes across multiple cancer types using gene expression data. ANetFL automaticallyadjusts the learning rate, manages class imbalance by addressing minority and hard-to-classify subtypes, and identifies key genescharacterizing cancer subtypes. In independent testing on 15 cancer types, ANetFL consistently achieved high accuracy across all metricsand outperformed existing methods. These findings suggest that ANetFL and the discovered key genes can support precise cancer subtypeclassification, facilitating the selection of more effective and personalized therapeutic strategies.