Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data

• URL: http://arxiv.org/abs/2608.13256v1
• Date: Thu, 13 Aug 2026 13:57:47 GMT
• Title: Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
• Abstract summary: We present a comparative analysis of generative models for transcriptomic data, investigating strategies to incorporate prior biological knowledge via gene graphs.<n>Among the alternatives, MK-TGAN stands out for its performance in terms of both the realism and utility of the generated data.<n>Our results show that prior knowledge integration strategies improve performance, and that MK-TGAN consistently produces synthetic samples with superior realism and biological plausibility.
• Score: 0.20999222360659606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-quality data. Synthetic data generation can help overcome these limitations. Here, we present a comparative analysis of generative models for transcriptomic data, investigating strategies to incorporate prior biological knowledge via gene graphs. This ensures that synthetic data capture real-world gene patterns, maintaining their usefulness for downstream tasks. In particular, we introduce and benchmark three variants of the Generative Adversarial Network. Among the alternatives, MK-TGAN - an innovative multi-kernel, Graph Neural Network-based model - stands out for its performance in terms of both the realism and utility of the generated data. Unlike other methods, MK-TGAN leverages prior knowledge graphs by exploiting graph neural networks. Our results show that prior knowledge integration strategies improve performance, and that MK-TGAN consistently produces synthetic samples with superior realism and biological plausibility.

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