Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
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 cap
Record details
Published: 13 August 2026
Source: arXiv
Category: Research
Topics: Finance, VC & PE · Biotech
Retrieved: 14 August 2026
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ethics.ai (13 August 2026), “Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data,” evidence record 19174, https://ethics.ai/record/19174 (originally published by arXiv).
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