Few-Shot Learning for Multi-Omics Disease Classification with a MAML-Based Mode 


Vol. 14,  No. 7, pp. 555-561, Jul.  2025
https://doi.org/10.3745/TKIPS.2025.14.7.555


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  Abstract

Few-shot learning offers a promising approach for disease classification in settings where labeled data are scarce. While widely explored in cancer research, its application to non-cancer diseases and multi-omics data remains limited. In this study, we propose a MAML-based few-shot learning model, pre-trained on TCGA data from four distinct tissue types. We then evaluate its adaptability across three disease categories, including COVID-19, Cirrhosis, and HBV-HCC. Our results demonstrate that MAML consistently outperforms a baseline MLP, achieving higher PR-AUC and ROC-AUC for COVID-19 and Cirrhosis. However, for HBV-HCC, where disease characteristics closely align with the pre-training data, the baseline MLP exhibits slightly superior performance. These experimental results suggest that disease classification is feasible even with multi-omics data collected under limited sampling conditions. Furthermore, this study identifies disease groups where the advantages of MAML can be effectively leveraged.

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[IEEE Style]

G. Yi, J. Ju, K. Jo, "Few-Shot Learning for Multi-Omics Disease Classification with a MAML-Based Mode," The Transactions of the Korea Information Processing Society, vol. 14, no. 7, pp. 555-561, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.7.555.

[ACM Style]

Ganghui Yi, Jeongjin Ju, and Kyuri Jo. 2025. Few-Shot Learning for Multi-Omics Disease Classification with a MAML-Based Mode. The Transactions of the Korea Information Processing Society, 14, 7, (2025), 555-561. DOI: https://doi.org/10.3745/TKIPS.2025.14.7.555.

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