Title: SHAP zero Explains Biological Sequence Models with Near-zero Marginal Cost for Future Queries
Venue: NeurIPS 2025
Accepted: yes
Keywords: Fourier transform

Möbius transform

Shapley values

Shapley interactions

Biological sequences

Genomics

Proteins
Abstract: The growing adoption of machine learning models for biological sequences has intensified the need for interpretable predictions, with Shapley values emerging as a theoretically grounded standard for model explanation. While effective for local explanations of individual input sequences, scaling Shapley-based interpretability to extract global biological insights requires evaluating thousands of sequences—incurring exponential computational cost per query. We introduce SHAP zero, a novel algorithm that amortizes the cost of Shapley value computation across large-scale biological datasets. After a one-time model sketching step, SHAP zero enables near-zero marginal cost for future queries by uncovering an underexplored connection between Shapley values, high-order feature interactions, and the sparse Fourier transform of the model. Applied to models of guide RNA efficacy, DNA repair outcomes, and protein fitness, SHAP zero explains predictions orders of magnitude faster than existing methods, recovering rich combinatorial interactions previously inaccessible at scale. This work opens the door to principled, efficient, and scalable interpretability for black-box sequence models in biology.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors introduce SHAP zero, a fast approximate method for computing SHAP values for model explainability based on sparse Fourier transforms and near-zero marginal cost for queries over large biological sequence datasets. The method is evaluated on three sequence-based models for predictive tasks related to guide RNA efficacy, DNA repair, and protein fitness. SHAP zero shows comparable results to KernelSHAP, while being many times faster.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nThe method is mainly compared against KernelSHAP, and seems to give a significant speedup with comparable results for sequence-based models.\nModel explainability is an important and understudied problem for biological sequence models.\n\nWeaknesses\nThe notation for Shapley values is appreciated, but probably better suited to a Methods or Preliminaries section than almost immediately in the Introduction.\nThe main text would benefit from pseudocode showing the SHAP zero algorithm.\nOverall, the connections between the sparse Fourier transform, Mobius transform, and SHAP values are mathematically clear, but not intuitively clear. In addition to writing out the algorithm, it may be beneficial to simply explain how traditional SHAP and SHAP zero differ.\nThere is no mention of gradient-based attribution methods, like integrated gradients, which are possibly the most widely adopted and easy to use methods for explaining sequence-based models.\n\nQUESTIONS:\nThe manuscript repeatedly refers to a model “sketch”, and specifies that the Fourier transform of a black-box model f is the “global sketch” of that model. What is the exact definition and meaning of “sketch” in this context?\nWhat is the rationale for excluding gradient-based attribution methods, considering that all of the models f in the work are differentiable neural networks?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SHAP zero, an algorithm for explaining black-box biological sequence models. By amortizing the computational cost of calculating Shapley values across many queries, the method enables large-scale interpretability studies that were previously intractable. The main idea is to create a one-time global sketch of the model using a sparse Fourier transform, from which local explanations for future queries can be derived at near-zero marginal cost. The authors demonstrate its effectiveness on models for gRNA efficacy, DNA repair, and protein fitness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The manuscript is well-written and presents an exciting application of model interpretability to the important domain of biological sequences. The connection established between Shapley values, the Möbius transform, and the sparse Fourier transform is elegant and provides a strong theoretical foundation for the proposed method.\n- The case study involving the Tranception model is interesting. The method demonstrates a promising capability to uncover the mechanistic basis of epistasis in Green Fluorescent Protein (GFP), a task of significant biological interest.\n\nWeaknesses\n- While the results are promising, the claims about capturing epistatic interactions would be substantially strengthened by including a broader range of examples. The paper presents a single case study on GFP using the Tranception model. To more robustly validate the method's ability to uncover epistasis, it would be beneficial to apply it to other proteins with well-characterized or computationally predicted epistatic networks\n- A fundamental challenge is the evaluation of the generated explanations. The manuscript demonstrates that the explanations align with some known biological phenomena, but the concept of a \"ground truth\" for interpretability remains elusive. How can we quantitatively assess the correctness of an explanation, especially in cases where the underlying biological mechanisms are not fully understood by human experts? Without a clear evaluation framework, the interpretations risk being perceived as plausible yet speculative hypotheses.\n\nQUESTIONS:\n- In the analysis of the Tranception model, the paper notes the use of \"Tranception with retrieval\". This model's function is not a simple mapping from a sequence X to a real value R, i.e., f:X→R. Instead, it incorporates information from a MAS, making the function closer to f:(X,M)→R, where M represents the MSA. Could the authors clarify how this additional input from the MSA space was handled within the SHAP zero framework?\n\n- The validation is focused on DNA and protein sequence models. Could the authors comment on the potential applicability of SHAP zero to other types of biological data that also have a combinatorial nature but a different structure, such as protein-protein interaction networks or models of RNA-protein binding?\n\nLIMITATIONS:\n- The authors have provided a thoughtful discussion of the limitations of their work, particularly regarding the assumptions of Fourier sparsity and the uniform marginalization strategy. I find this discussion to be transparent and appropriate.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors describe a method for computing Shapley values for biological sequence-to-function models with query-only access that scales significantly better than existing exponential-time methods. It achieves this by paying an upfront polynomial-time cost to create a global Fourier sketch of the model, which is tractable for biological sequence contexts given their sparsity. The authors then show that for a given sequence, the Fourier coefficients can be mapped to Shapley values via a Mobius transform that scales with O(q^l), where q is the dimensionality of the input space (e.g. 4 for nucleic acids and 20 for proteins) and l is the maximum order of interactions considered. To do this, the authors introduce a q-ary extension of their previously-published binary Mobius transformation. The authors test their method on three existing sequence-to-function models (TIGER, inDelphi, and Transception). For TIGER and inDelphi, they show that their Fourier coefficients predict the test set better than linear or pairwise models and that their calculated Shapley values correlate well with those produced by KernelSHAP and SHAP-IQ while being orders of magnitude faster. For Transception, the authors additionally found epistatic interactions between certain residues of GFP.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper was clear and well-motivated.\n- The authors benchmarked their method in three different biological contexts, covering RNA, DNA, and protein sequences.\n- Their q-ary Mobius transform allows for amortization in biological sequence contexts, filling in a “missing link”, so to speak.\n- The paper compares to FastSHAP, which uses a neural network to amortise, and provides some insight into why FastSHAP does not work very well for biological sequence models.\n- SHAP zero is impressively fast while maintaining decent-to-great correlation with slower methods, opening the door to interpreting sequences-to-function models that might have been intractable for slower methods.\n- Theoretical analysis of the approach is provided, filling in details such as complexity.\nWeaknesses:\n- The sparse Fourier algorithm assumes equal probability of all feature subsets, which doesn’t reflect real sequence distributions. The authors recognize this as a limitation when they note that KernelSHAP has more negative values for rare token values, compared to SHAP zero, but they don’t fully flesh out the implications for applying their method since rare nucleotides/amino acids usually reflect strong negative selection. For example, it would be helpful for potential users to know how their method labels stop codons/CpGs/etc to aid in interpreting results.\n- The major advantage of SHAP zero is the ability to assign Shapley values in contexts that were previously intractable, so it seems like a missed opportunity that the authors don’t apply their method to any such context since that’s where future users of the tool will likely want to use their method (the Transception case study is still in the context of a small GFP landscape).\n\nQUESTIONS:\nHow does SHAP zero perform when asked to evaluate rare and highly deleterious mutations? Even if it doesn’t assign as highly-negative of values as a method like KernelSHAP, does it still correctly identify those mutations as being the most explanatory?\n\nHow does SHAP zero perform in the context of a sequence landscape that would be computationally-intractable for methods like KernelSHAP and SHAP-IQ? Suggestions include models trained on MPRA datasets or even a synthetic dataset where all the feature contributions and higher-order interactions are known. Even though it would be impossible to compare to slower methods, it would add to the paper’s significance if the method was able to pull out known feature contributions/interactions while also identifying new ones.\n\nThe Tranception case study is the least fleshed-out and most confusing, especially for epistatic interactions. The statement in the 4d figure legend “SHAP zero reveals numerous epistatic interactions with Proline (P) and Lysine (K).” is not actually shown in the figure (that I can tell) and needs to be inferred from Appendix D tables. It’s also unclear why the correlations with SHAP-IQ values are relatively low, and there is no analysis of whether the Fourier coefficients predict mutational effects better than a linear or pairwise baselines, so while SHAP zero is pulling out interactions, it’s hard to evaluate how trustworthy those conclusions are.\n\nModel evaluation cost:  The initial sketch requires many queries for TIGER/inDelphi (appendix). For large transformer models whose forward pass dominates runtime, the wall‑clock break‑even point (e.g. 25 k sequences) may be restrictive for some users. Discussion of adaptive or incremental sketching could be beneficial.\n\nLIMITATIONS:\nYes, the authors adequately addressed the limitations of their work, except for some points that were addressed earlier.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work develops a new approach for SHAP values estimation in black box biological sequence models, focusing on amortisation of computational cost. The approach is to, instead of querying SHAP values for every sequence of interest, build a global sparse representation of the model, which only needs to be done once, using a sparse Fourier transform, and connect this to sequence-specific SHAP values via a Möbius transformation. They test the method's ability to explain guide rNA binding (with TIGER), DNA repair outcome (with inDelphi), and epistasis in protein language models (with Tranception). They show good agreement with SOTA approaches, both at extracting SHAP values and high-order interactions, at a much lower computational cost, especially in the regime of small sequences and large number of queries. The article is well written and clear.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nInteresting idea, well exposed, with potential to be portable across multiple tasks with biological sequence models and have therefore broad adoption. The approach is technically solid and the experiments are for the most part sufficiently appropriate to be convincing of this strategy's value. I find it particularly interesting that the Fourier coefficients recovered from SHAP zero perform so well at the RNA binding and DNA repair tasks, strongly suggesting that this is a meaningful representation of the data. \n\nWeaknesses:\nThe approach seems to have benefits in computational costs whenever the number of queries is large, the function sufficiently sparse, and the length of the input sequence is not too large. This questions how broadly adopted this strategy can be in practice. \n\nI found that the protein language model experiment could have been taken further to be more representative of the kinds of tasks the community is interested in (see questions).\n\nQUESTIONS:\n1. If I understand correctly, you are querying variation on 10 positions known to be epistatic in GFP. The first question is how does your Fourier representation correlate with experiments, compared to Tranception, linear and pairwise representations (an equivalent to 2b and 3b)? Maybe I missed it. \nI think that perhaps a more interesting question than looking at known interacting positions would be to, given many positions, identify the ones that are epistatically interacting. This would be more in line with tasks such as identifying allosteric sites involved in ligand binding, protein design, etc. Would this be computationally viable? (you would increase sequence length but would look at a sparser space) See question 2, related.\n2. How far in sequence length can you take this approach? I think a scalability analysis in a protein and RNA/DNA setting would be useful. \n3. Have you considered using other sparse representations such as wavelets instead of a sparse Fourier basis? What do you think would be the relative pros and cons? \n4. If I understand correctly, in line 223 you say that the overlapping high-order feature interactions between SHAP zero and SHAP-IQ correspond to 95% of the total SHAP zero high-order feature interactions and 5% of those from SHAP-IQ. Why the difference in total number of high-order interactions between these two approaches? Also, why is it the opposite in the DNA repair example, line 244?\n\nLIMITATIONS:\nIt would be useful to have a better picture of the scalability of the approach as mentioned in question 2.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}