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Structure-Aware Synthetic Data Generation for Imbalanced Biological Machine Learning: A Review of Clustering-Guided Approaches

2026-08-26

Abstract excerpt

Machine learning has become a central framework for analyzing high-dimensional biological data generated by genomics, transcriptomics, proteomics, single-cell sequencing, and multi-omics technologies. However, predictive modeling is frequently limited by severe class imbalance, where biologically important populations including rare disease subtypes, uncommon molecular states, and low-frequency cell populations ar...

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Literature Corpus work: c81beed5-46c4-57f2-968a-d7870015e293
DOI: 10.20944/preprints202608.1908.v1

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