We are also curious to learn new biology when black box models outperform IQ. In CRE sequences alone, LLMs have not yet revealed grammars that transcend IQ’s simple TF-DNA interactions. Longer-range chromosomal interactions among CREs, we believe, may be a different story.
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We are excited about the possibility of using IQ to develop better DL for epigenomes. We do gain from using ensembles of IQ and DL models – so there is hope! LLMs consider larger contexts than IQ, while IQ is super economical in parameters and may generalize better because of that.
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Insight 3: Local interactions between TFs. IQ predicts TF interactions that predict CRE accessibility across differentiation trajectories. For example: Mesp-Eomes motif co-occurences may be important for germ-layer specification.
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Insight 2: TFs care about sub-optimal binding sites. IQ integrates strong and weak TF-DNA interactions. This reveals that TFs 'read' sequences in different ways – some care only for the best targets and others integrate many weak ones.
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Here are 3 biological insights derived from IQ (before we reflect on what’s in it for the non-biologist crowd).
Insight 1: Sequence defines regulatory intensity over a quantitative spectrum – not a binary yes/no classifier. IQ can predict this spectrum!
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For example, when modelling regulation of Epiblast to Mesoderm differentiation in mouse embryos, we can explain changes in accessibility with only 13 TF motif models!
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IQ inference starts from detailed model and progressively simplifies it to a small set of physical models. Models with smaller number of components are easier to interpret. They also generalize better.
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IQ regresses AP from sequence using biophysically inspired TF binding models including:
*Spatial integration across a range of TF-DNA affinities
*Latent TF concentrations with non-linear dose-response
*Synergistic/antagonistic pairwise TF interactions
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IQ uses a normalization trick based on coverage of constitutive ATAC peaks to derive APs robustly. Because AP is defined on an absolute scale – comparing AP among conditions is immediate and robust.
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But IQ is more than a predictive tool. A first important difference between IQ and other approaches is the transformation of ATAC-seq coverage to access probabilities (AP) - the instantaneous chances to find a CRE in an open state among cells from a given type.
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As a mere predictive tool, IQ performs on par with state-of-the-art DL models such as Borzoi and DeepTopic. Evaluation on human blood and mouse embryo datasets shows that current best performance is derived using an ensemble of IQ and Borzoi.

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Do we need LLMs to predict epigenomes from DNA—or is biophysics enough? 🧬
IceQream (IQ) is a biophysics-based framework that predicts epigenomes with SOTA-level accuracy—and is fully explainable.
@NatureComms: www.nature.com/articles/s41...
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