AI models predict RNA isoform diversity: systemic bias in genomic research overlooks 90% of human transcriptome variability
Original framing: “AI-driven framework enables precise prediction of RNA splicing and isoform usage” — Phys.org
Scientifically, the AI-driven approach to RNA splicing prediction is grounded in machine learning models trained on large datasets, but it risks oversimplifying biological complexity. The focus on 'precise prediction' overlooks the stochastic nature of splicing, which is influenced by environmental factors, epigenetic modifications, and cellular context. While AI can identify patterns, it often fails to capture the dynamic, context-dependent nature of RNA isoform usage, which is critical for understanding disease and evolution. The scientific community's emphasis on canonical splicing events also reflects a bias toward 'druggable' targets, sidelining the 90% of transcriptome variability that remains understudied.
The AI-driven RNA splicing narrative exemplifies how modern genomics, while technologically advanced, remains trapped in reductionist and Eurocentric paradigms that obscure 90% of human transcriptome variability.
This oversight is not accidental but structural, reflecting the dominance of Western biomedical institutions and their preference for 'druggable' targets over holistic health. The exclusion of indigenous knowledge—such as Māori whakapapa or Ayurvedic prakriti—further entrenches this bias, reducing genetic diversity to a computational problem. Historically, this mirrors the Human Genome Project's Eurocentric bias, where a narrow focus on a few individuals sidelined global genetic diversity. A systemic solution requires decolonizing genomic research by integrating indigenous knowledge, adopting systems biology frameworks, and reforming funding priorities to prioritize equity and adaptability. Only then can genomics move beyond precision to embrace the complexity of living systems, where RNA splicing is not just a computational challenge but a reflection of cultural, ecological, and spiritual interconnectedness.