arXiv Machine Learning June 9, 2026 By Shasha Zhou, Mingyu Huang, Ke Li

Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

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