Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods
arXiv:2606.07607v1 Announce Type: new Abstract: Advances in machine learning and computational power have unlocked the predictive potential of the human genome, yet biologists now demand that these models also elucidate the underlying biological mechanisms. While interpretable machine learning (IML) techniques have been increasingly applied to bridge this gap, there has been a pervasive reliance on anecdotal validation: the vast majority of research relies on a single IML method and reports only isolated successful instances. Through a benchmarking study on transcription factor binding, we dem
The increasing sophistication of AI models in genomics necessitates a more rigorous and standardized approach to interpretability, moving beyond anecdotal evidence to ensure wider acceptance and application.
This paper highlights a critical need for robust validation of interpretable machine learning in genomics, which is essential for translating predictive AI into actionable biological insights and therapeutic development.
The focus shifts from simply demonstrating isolated successes of IML methods to demanding systematic benchmarking and validation, raising the bar for research quality and clinical applicability.
- · Bioinformaticians with strong validation methodologies
- · Genomic data platforms
- · AI ethics and safety researchers
- · Drug discovery and development
- · Researchers relying on anecdotal IML validation
- · Models lacking explainability
- · Purely predictive genomic AI without mechanistic insight
Increased rigor in interpreting AI models for genomic research will accelerate the discovery of actual biological mechanisms.
Standardized interpretability benchmarks could become a prerequisite for funding and publication in leading genomic and AI journals.
More reliable and interpretable genomic AI models could significantly de-risk and speed up the development of precision medicines and synthetic biology applications.
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Read at arXiv cs.LG