This program supports AI startups at any time of the year. Benefit from cutting-edge resources and tailored support to accelerate your technology's development.
Offered by Mila and the Public Policy Forum, this program is designed to equip policy and decision makers with the tools to navigate the opportunities and risks of AI. The next cohort will be held in French on September 1-2, 2026, at Mila.
Connect with a Mila academic advisor and current student-researchers to learn more about Mila's community and how to join us on August 19, 31 and September 11, 2026.
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MOTIVATION: Genome sequence-to-function (S2F) models are widely used to interpret base-resolution functional genomics assays. Most S2F model… (see more)s are trained and evaluated against observed counts and profile-shapes using statistical objectives and fidelity metrics. These choices are well motivated, but they are DNA-independent. At the same time, experimental measurements arise from DNA-dependent assays with distinct characteristics. This mismatch motivates a complementary DNA-aware evaluation of S2F-predicted and experimental functional genomic tracks. RESULTS: We study DNA-dependency of experimental and S2F-predicted tracks using track-conditional genome language models (cgLMs). cgLMs predict masked nucleotides from a conditioning track under controlled DNA visibility. Across ATAC-seq and TF ChIP-seq peaks from GM12878 and K562, cgLM-probing reveals a consistent masked DNA-decodability gap between many experimental and S2F-predicted tracks. In particular, single-task (e.g. BPNet) and multi-task (e.g. AlphaGenome) S2F-predicted tracks enabled cgLMs to recover masked nucleotides with significantly higher accuracy and confidence than matched experimental tracks. Analyses of nonpeak and dinucleotide-shuffled sequences show that this gap is not confined to peaks and is not captured by standard DNA-agnostic profile-shape fidelity metrics alone. ChromBPNet Tn5-denoised predictions were an exception and behaved closer to the experimental regime, suggesting that staged training may reduce the gap. We then convert this diagnostic into a critic-derived objective, DNA-dependency matching (DDM), using a frozen multi-headed cgLM critic. We introduce Critic-Guided Profile-Shape Editing (CGPSE), a preliminary post hoc debiasing framework for frozen S2F models. In GM12878 ATAC-seq, CGPSE partially reduces the masked DNA-decodability gap for AlphaGenome and BPNet predictions, while exposing a tradeoff with profile-shape fidelity. AVAILABILITY AND IMPLEMENTATION: https://github.com/li-lab-mcgill/dna-aware-s2f-eval.
Differential Chromatin Architecture and Risk Variants in Deep Layer Excitatory Neurons and Grey Matter Microglia Contribute to Major Depressive Disorder