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REVIEW 2 major objections 6 minor 75 references

Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking

T0 review · 2 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This review and benchmark finds that SHAP and sensitivity analysis, among four explainability methods for graph-based multimodal biomedical models, recover the broadest set of known Alzheimer's pathways and Gene Ontology terms.

desk verdict Useful taxonomy plus a narrow but honest benchmark; the method ranking is conditional on a post-hoc biological label, so treat it as a case study, not a definitive comparison. read the letter →

arxiv 2505.01696 v1 pith:X7DPYGVI submitted 2025-05-03 q-bio.GN cs.AI

Alireza Sadeghi , Farshid Hajati , Ahmadreza Argha , Nigel H Lovell , Min Yang , Hamid Alinejad-Rokny This is my paper · ORCID
classification q-bio.GNcs.AI
keywords GraphneuralnetworksMultimodalmedicaldataInterpretablemodelsExplainableartificialintelligenceKnowledge-guideddeeplearningLLM-integratedAlzheimer'sdiseasebenchmarkGeneOntologyanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that interpretable graph-based models for multimodal biomedical data form a distinct technical area and organizes that area into four interpretability families: modality or feature elimination, non-graph explainability methods, graph-native explainers, and inherently interpretable designs. Surveying 26 studies from January 2019 to September 2024, it finds that most work targets disease classification, builds static graphs from similarity measures, and leans on post-hoc methods borrowed from non-graph domains. The paper then benchmarks four explainability techniques on an Alzheimer's disease cohort using a replicated multi-omics graph model. It reports that SHAP and sensitivity analysis recover the broadest set of known Alzheimer's pathways and Gene Ontology terms, while gradient saliency and graph masking surface complementary metabolic and transport signatures. A reader should care because the review offers a method-selection map for researchers who need transparency in clinical graph models.

What carries the argument

The load-bearing machinery is the four-category interpretability taxonomy combined with a benchmark protocol that ranks explainers by the biological plausibility of their top gene sets. The taxonomy separates modality or feature elimination, non-graph XAI such as gradient saliency, sensitivity analysis, and SHAP, graph-based XAI such as graph masking, GNNExplainer, and top-k pooling, and inherently interpretable designs such as attention weights, graph attention, filter weights, and decision trees. The benchmark then applies representative methods from Categories II and III to a replicated MOGONet graph model, a multi-omics graph convolutional network fusing mRNA, methylation, and miRNA views, on the ROSMAP Alzheimer's dataset, and evaluates the top 30 genes from each method through over-representation analysis against known GO terms and pathway databases at a $p \le 0.01$ threshold.

What would settle it

Re-run the same four explainers on a second disease whose pathway knowledge is independently curated, or hold out a later-curated set of Alzheimer's genes; if SHAP's and sensitivity analysis's overlap advantage over gradient saliency and graph masking disappears or reverses, the reported ranking is an artifact of the specific knowledge base rather than a property of the methods.

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Extended reading notes

Core claim

The central claim is that the interpretability landscape for graph-based multimodal biomedical models can be usefully divided into four families, and that within these families there is no single best explainer: the right choice depends on whether a study values breadth of known disease biology, discovery of complementary signatures, or computational cost. On the paper's Alzheimer's benchmark, SHAP and sensitivity analysis identified the most established AD-related GO terms and pathways, including GPCR signalling and neurodevelopmental processes, whereas gradient saliency and graph masking highlighted metabolic and transport biology such as 14-3-3 protein binding and SLC-mediated transmembrane transport. Permutation tests showed that all four methods' top-30 gene sets carried average importance scores above random gene sets, with gradient saliency showing the largest offset from the null. The paper also argues that modality elimination, a common Category I technique, is unfair for graph models when edges depend on node features, because removing a modality can rebuild the graph and change what is being compared.

Load-bearing premise

The benchmark trusts that the curated set of already-known Alzheimer's Gene Ontology terms and pathways is a correct yardstick for explanation quality, so an incomplete or biased disease-knowledge list would weaken its ranking of SHAP and sensitivity analysis.

Editorial extensions

If this is right

  • Researchers building graph models for clinical use can choose an explainer by goal: SHAP for broad disease-pathway coverage, sensitivity analysis for fast broad coverage, gradient saliency for speed, and graph masking for deeper but costlier biology.
  • The review's taxonomy gives method developers a common vocabulary for comparing future explainers against four distinct interpretability strategies.
  • The identified gaps, namely graph-native explainers, knowledge-graph edges, dynamic topology, and graph-in-graph hierarchies, mark concrete opportunities for the next generation of models.
  • The benchmark's compute-cost and biological-depth trade-offs can inform resource budgeting, since full SHAP runs on hundreds of features can take weeks.
  • The finding that all four methods beat random gene sets supports using any of them as a starting point, but with different expectations about which biology they will surface.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A reader should treat the 'beat random' permutation result as a sanity check rather than a faithfulness proof: it shows top genes carry non-random signal, not that the signal matches the model's actual reasoning.
  • The same four-method comparison could be run on other well-curated disease cohorts to test whether SHAP's breadth advantage generalizes or only reflects the maturity of Alzheimer's pathway databases.
  • Combining one fast coarse explainer with one deep explainer may be a cheaper way to get both breadth and specificity, a strategy the paper hints at but does not itself benchmark.
  • The graph-in-graph and LLM directions the paper sketches could turn the taxonomy into a design template: if each patient is a node inside a cohort graph and an internal subgraph, then Category II and III explainers would need to report at two levels simultaneously, a testable extension no current study in the review addresses.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This manuscript combines a technical literature review with a small benchmarking study. The review surveys 26 papers (2019--September 2024) that apply interpretable graph-based models to multimodal biomedical data, classifies interpretability approaches into four families (modality/feature elimination, non-graph XAI, graph-based XAI, inherent interpretability), and summarizes trends in graph construction and model architectures. The benchmark replicates MOGONet on the ROSMAP Alzheimer's disease dataset and compares gradient saliency, sensitivity analysis, SHAP, and graph masking by ranking the top 30 genes from each method, testing those gene sets against random gene sets via permutation, and counting enrichment of Gene Ontology and pathway terms. The paper's central empirical claim is that SHAP and sensitivity analysis recover the broadest set of known AD-associated GO terms and pathways, while gradient saliency and graph masking surface complementary metabolic and transport signatures, with all four methods beating random gene sets.

Significance. If the benchmark's ground truth were established objectively, the paper would provide a useful, reproducible comparison of explainability methods for graph-based multimodal biomedical models, and the proposed four-category taxonomy plus the design flowchart would be a practical reference for practitioners. The review fills a plausible gap at the intersection of interpretability, graph-based modeling, and multimodal biomedical data, and it is one of few works to combine a literature survey with a concrete benchmark. Strengths include the transparent PRISMA-style selection process, the public availability of code and data on GitHub, and the use of permutation testing as a sanity check for gene rankings. The main weakness is that the benchmark's outcome measure ("Alzheimer-related" GO/pathway terms) is defined post hoc, which makes the headline ranking dependent on the authors' subjective labeling rather than an independent, pre-specified ground truth.

major comments (2)
  1. [Section 5.5, Figure 8c/d; Methods ("Gene ontology and pathway analyses")] The benchmark's central ranking of SHAP and sensitivity analysis over gradient saliency and graph masking is based on counts of "Alzheimer-related GO terms" and "pathway terms" that are not defined independently of the enrichment results. Section 5.5 states that SHAP identified ten of eighteen "Alzheimer-related GO terms (unique GO terms identified by all methods)" and seven of fifteen pathway terms, but these eighteen and fifteen terms are the union of terms enriched at p ≤ 0.01 by any method, and the "Alzheimer-related" label is applied post hoc in the text (e.g., "temperature homeostasis" and "endocrine system development"). This makes the enrichment count a circular measure: the outcome variable is constructed from the same enrichment results being ranked. In addition, the Methods section says terms were prioritized by FDR (p < 0.01), while the benchmark retains terms with raw p-values ≤ 0.01, and no multiple-testing correction is described. Please define an objective, pre-specified AD gene or term panel (e.g., from DisGeNET, OMIM, or a curated pathway database) and use a consistent significance threshold with appropriate correction before claiming the method ranking.
  2. [Section 5.5 ("Benchmarking XAI approaches on MOGONet") and Methods] No classification performance is reported for the replicated MOGONet model on the ROSMAP test set. The Methods describe the training protocol (two-phase training, learning rates, epochs) but do not give accuracy, AUROC, F1, or any comparison to the original MOGONet results. If the replicated model does not predict AD status well, the feature attributions produced by the four XAI methods may reflect a poorly fitted or non-predictive model, undermining the biological interpretation of the top genes. Please report the model's discriminative performance on the held-out test set (and, ideally, a comparison with the original MOGONet publication), and state whether the explained model is the one used for the enrichment analysis.
minor comments (6)
  1. [Methods ("Gene ontology and pathway analyses") vs. Section 5.5] The Methods state that all GO terms and pathways were prioritized based on False Discovery Rate (FDR) values (p < 0.01), but Section 5.5 and Figure 8c/d retain terms with raw p-values ≤ 0.01 without any multiple-testing correction. Please reconcile the threshold and clarify whether FDR or raw p-values were used.
  2. [Figure 8 caption] The Figure 8a caption says "SHAP and gradient saliency were roughly able to detect a combination of the most important genes found by other techniques," but the main text states that SHAP and sensitivity analysis share more genes, while gradient saliency highlights distinct genes (NPNT, SYTL1, ANKRD30B). The caption should match the text.
  3. [General] The name "Pfiefer" appears in Sections 5.2 and 5.4 (e.g., "Zhang et al. [28] and Pfiefer et al. [30]"), while the reference list and other occurrences use "Pfeifer." Please standardize the spelling.
  4. [Section 2 (Methodology)] The search window is stated as "January 1, 2019, and September 31, 2024"; September has 30 days. Please correct the date to September 30, 2024.
  5. [Section 5.5 and Figure 8d] The pathway analysis retention criterion "an overlap > 1" is undefined in the text. Please specify what "overlap" refers to (number of genes from the top-30 list in the pathway) and report how many pathways were considered before filtering.
  6. [Section 6 (Discussion)] The claim that "SHAP and graph masking required more processing time" is only supported by a vague remark that generating the SHAP report "could take several weeks" in the Discussion. Since computational cost is one of the reported trade-offs, please provide concrete runtime measurements for all four methods on the same hardware.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark compares XAI rankings against external GO/pathway annotations; no fitted parameter or self-citation chain is load-bearing.

full rationale

The paper's central empirical claim is the benchmark comparison of four XAI methods on a replicated MOGONet/ROSMAP AD classification model. The evaluation metric is enrichment of each method's top 30 genes in Gene Ontology and pathway databases (WebGestalt ORA), i.e., an external literature-based reference, not a quantity derived from the model's own fitted parameters. The 'Alzheimer-related GO terms' are post hoc labels assigned by the authors to the union of enriched terms, so the ranking could be challenged on grounds of benchmark validity or pre-registration, but that is a correctness/external-validity concern, not circularity: the outcome variable is not defined in terms of the XAI scores being compared. No parameter is fitted to a subset and then predicted; no 'uniqueness theorem' from the authors' prior work is invoked; self-citations [13,14] are background references to earlier genomics deep learning, not load-bearing premises. The reported permutation tests compare top-gene importance against a random null, which is an independent statistical check. Thus the derivation chain is self-contained and no circular step can be exhibited.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central benchmark rests on a small number of human-chosen thresholds and domain assumptions about the model, the disease knowledge base, and the enrichment tool. The review itself adds no new entities and no fitted model parameters beyond the analysis choices listed above.

free parameters (5)
  • Top-k gene list size = 30
    The authors rank features and take the top 30 per XAI method for overlap, permutation, and enrichment analysis (Section 5.5, Figure 8). The choice of 30 affects all downstream comparisons.
  • GO/pathway significance threshold = 0.01
    Only GO terms and pathways with p-values (described as FDR in the methods paragraph) at most 0.01 were retained, influencing which methods appear to recover known AD biology.
  • Number of random permutations = 1000
    The null distribution in Figure 8b samples 30 random genes 1,000 times; the significance of the observed ratios depends on this count.
  • SHAP weighted neighbors = 100
    Kernel SHAP generated 100 weighted neighbors per test sample before fitting a logistic regression; this is a computational approximation of exact Shapley values.
  • Graph masking optimization epochs = 100
    A random mask was optimized for 100 epochs per test sample; the quality of the learned mask and its gene rankings depend on this budget.
assumptions (5)
  • domain assumption MOGONet was correctly replicated on ROSMAP
    The benchmark conclusions assume that the reimplementation in the Methods paragraph Benchmarking XAI approaches on MOGONet faithfully matches Wang et al.'s architecture and training protocol; any mismatch would change feature importance.
  • domain assumption Known AD-associated GO and pathway terms are a valid quality benchmark for explanations
    In Section 5.5, top genes are judged by enrichment of established AD biology; if explanation quality should instead be judged by causal or counterfactual correctness, the ranking of methods could change.
  • domain assumption The PRISMA-style literature search correctly identified all relevant studies
    The first-technical-review claim depends on the completeness and correctness of the keyword search and screening from 2,786 to 26 papers; search date or keyword gaps would weaken that claim.
  • domain assumption WebGestalt ORA with the selected databases is a suitable enrichment method
    The GO and pathway results rely on WebGestalt's over-representation analysis and its background; different tools or backgrounds could alter which terms are significant.
  • domain assumption The four XAI implementations match their canonical methods
    The comparison assumes the gradient saliency, SHAP, sensitivity analysis, and graph masking implementations used in the benchmark are faithful to the methods as cited and do not contain implementation artifacts.

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Cite this review

Pith. "Pith review of Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking." pith.science (2026). https://pith.science/paper/X7DPYGVI

@misc{pith2026250501696,
  author       = {Pith},
  title        = {Pith review of: Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X7DPYGVI}},
  note         = {Machine review of arXiv:2505.01696}
}
read the original abstract

Integrating heterogeneous biomedical data including imaging, omics, and clinical records supports accurate diagnosis and personalised care. Graph-based models fuse such non-Euclidean data by capturing spatial and relational structure, yet clinical uptake requires regulator-ready interpretability. We present the first technical survey of interpretable graph based models for multimodal biomedical data, covering 26 studies published between Jan 2019 and Sep 2024. Most target disease classification, notably cancer and rely on static graphs from simple similarity measures, while graph-native explainers are rare; post-hoc methods adapted from non-graph domains such as gradient saliency, and SHAP predominate. We group existing approaches into four interpretability families, outline trends such as graph-in-graph hierarchies, knowledge-graph edges, and dynamic topology learning, and perform a practical benchmark. Using an Alzheimer disease cohort, we compare Sensitivity Analysis, Gradient Saliency, SHAP and Graph Masking. SHAP and Sensitivity Analysis recover the broadest set of known AD pathways and Gene-Ontology terms, whereas Gradient Saliency and Graph Masking surface complementary metabolic and transport signatures. Permutation tests show all four beat random gene sets, but with distinct trade-offs: SHAP and Graph Masking offer deeper biology at higher compute cost, while Gradient Saliency and Sensitivity Analysis are quicker though coarser. We also provide a step-by-step flowchart covering graph construction, explainer choice and resource budgeting to help researchers balance transparency and performance. This review synthesises the state of interpretable graph learning for multimodal medicine, benchmarks leading techniques, and charts future directions, from advanced XAI tools to under-studied diseases, serving as a concise reference for method developers and translational scientists.

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.