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arxiv: 2605.03690 · v2 · pith:GUMD27NWnew · submitted 2026-05-05 · 💻 cs.LG · cs.AI· q-bio.QM

Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

Filip Kronstr\"om , Alexander H. Gower , Daniel Brunns{\aa}ker , Ievgeniia A. Tiukova , Ross D. King This is my paper

Pith reviewed 2026-05-21 08:16 UTC · model grok-4.3

classification 💻 cs.LG cs.AIq-bio.QM
keywords graph neural networksknowledge graphssemantic lossontologyyeast phenotypesgene knockoutsbox embeddings
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The pith

Incorporating semantic loss from ontologies into GNN embeddings of knowledge graphs improves yeast phenotype predictions.

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

The paper develops a method for hierarchy-aware embeddings of knowledge graphs by enriching graph neural networks with semantic loss terms from ontologies. This produces embeddings that better match domain knowledge structures. Applied to a yeast knowledge graph built from community data, the approach predicts cell growth phenotypes after double gene knockouts with a mean R² of 0.360. Adding the semantic loss boosts performance to 0.377 and allows generalization to unseen triple knockouts. The method also extracts co-occurring relations to form biological hypotheses that can be tested experimentally.

Core claim

Graph neural networks enriched with semantic loss derived from underlying ontologies yield hierarchy-aware embeddings of knowledge graphs. When used with low-dimensional box embeddings to predict yeast cell growth for double gene knockouts, these models achieve a mean R² score of 0.360 over 10-fold cross validation, significantly higher than baselines. Incorporating semantic loss terms improves this to R²=0.377 by aligning embeddings with ontology structure, showing that class hierarchies can be exploited for quantitative prediction. The models generalize to triple gene knockouts, and important relations identified help generate and validate biological hypotheses.

What carries the argument

Graph neural networks combined with semantic loss from ontology hierarchies to produce low-dimensional box embeddings that reflect class structures in the knowledge graph.

If this is right

  • The embeddings enable prediction of cell growth after double gene knockouts with R² around 0.36, outperforming baselines.
  • Semantic loss improves predictive performance to R²=0.377 by aligning with ontology hierarchies.
  • The models generalize to triple gene knockouts beyond the training data.
  • Co-occurring relations in the KG can be used to construct and experimentally validate hypotheses about yeast traits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could extend to phenotype prediction in other model organisms or even human genetics.
  • Box embeddings might serve as a tool for systematically revising and improving knowledge graphs in various domains.
  • Integrating such embeddings could help prioritize experiments by predicting likely outcomes of gene interactions.

Load-bearing premise

The yeast knowledge graph constructed from community databases and ontology terms contains the biologically relevant relations determining cell-growth phenotypes after gene deletions.

What would settle it

Measuring that adding semantic loss terms does not increase or even decreases the R² score on yeast phenotype prediction, or that predictions fail on triple gene knockouts.

Figures

Figures reproduced from arXiv: 2605.03690 by Alexander H. Gower, Daniel Brunns{\aa}ker, Filip Kronstr\"om, Ievgeniia A. Tiukova, Ross D. King.

Figure 1
Figure 1. Figure 1: Overview of the Hierarchy-aware GNN. a shows how the output of each message passing layer, which aggregates information between neighbours in the KG, is treated as a latent variable that is converted into boxes through the box trans￾formation in (6). The boxes are trained to fulfil specified class hierarchies using the losses in (10-13), which can also be applied to the prior node embedding. The output of … view at source ↗
Figure 2
Figure 2. Figure 2: An overview of the different types of classes and how they are connected in the knowledge graph is shown in a. The colour of the nodes specifies where the classes are defined. b shows examples from the hierarchies defining classes in the domains introduced in Section 5.3 view at source ↗
Figure 3
Figure 3. Figure 3: An overview of the system predicting the fitness when deleting pairs of genes is shown in a. A GNN using GraphSAGE message passing layers, acting on the KG from Section 5.2, generates node embeddings. The embeddings of pairs of genes are combined through element-wise multiplication and fed to a NN predicting the fitness of the gene deletion. b shows how classes in the different domains are represented by b… view at source ↗
Figure 4
Figure 4. Figure 4: Parity plots for double, (a) and (c), and triple, (b) and (d), gene deletions. (a) and (b) shows the parity plot for the model using box embeddings as prior node representations only, while (c) and (d) shows the predictions from a model also trained with the distance-based semantic losses, Ldistance. For the double dele￾tion, the predictions from all validation sets in the cross validation are shown. ure 4… view at source ↗
Figure 5
Figure 5. Figure 5: Average Ldistance and L − distance losses per class for the different domains in the KG, during training of the best performing model (f) in view at source ↗
Figure 6
Figure 6. Figure 6: An overview of the selection and results of the experiment we performed. (a) shows the highest ranked importances of edge-pairs and the pair selected for the experiment, nutrient utilisation of inositol and stress resistance to NaCl, is highlighted in red. f0 and f1, which have a higher assigned weight, are discarded due to safety and lab constraints as it involves the chemical bleomycin. (b) Box plot show… view at source ↗
Figure 7
Figure 7. Figure 7: Learned box embeddings in two dimensions for the molecular function domain. 7(a) and 7(b) box embeddings prior to input into GNN; 7(b) and 7(d) show final embeddings for distance and overlap loss respectively. The main advantage of our approach is that signals from semantic information encoded in class hierarchies, and signals from predictive tasks can jointly be used to train graph neural networks. We dem… view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of distances of box embeddings learned from revised graphs G˜ to the original embeddings learned from Gtrain, shown by relation type, for a subset of the relation types in the graph. (The method for calculating these differences is described in 5.6). For most relations, the distance ranks of randomly drawn edges (constrained to the appropriate class) were significantly different to the ranks o… view at source ↗
Figure 9
Figure 9. Figure 9: Growth curves showing the mean optical densities of the 6-8 repetitions for the different experimental groups. Optical density (at 600nM) is a unitless measure￾ment typically used as an indirect measure of cell density and biomass. 48 view at source ↗
read the original abstract

We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontologies. This method yields embeddings that better reflect domain knowledge. To demonstrate their utility, we predict and interpret the effects of gene deletions in the yeast Saccharomyces cerevisiae and learn box embeddings for KGs in the absence of a prediction task. We further show how box embeddings can serve as the basis for evaluating KG revisions. Our yeast KG is constructed from community databases and ontology terms. Low-dimensional box embeddings combined with GNNs are used to predict cell growth for double gene knockouts. Over 10-fold cross validation, these predictions have a mean $R^2$~score~of~0.360, significantly higher than baseline comparisons, demonstrating that high-level qualitative knowledge is informative about experimental outcomes. Incorporating semantic loss terms in the training of the models improves their predictive performance ($R^2$=0.377) by aligning embeddings with ontology structure. This shows that class hierarchies from ontologies can be exploited for quantitative prediction. We also test the trained models on triple gene knockouts, showing they generalise to data beyond those seen in training. Additionally, by identifying co-occurring relations in the yeast KG important for the cell-growth predictions, we construct hypotheses about interacting traits in yeast. A biological experiment validates one such finding, revealing an association between inositol utilisation and osmotic stress resistance, highlighting the model's potential to guide biological discovery.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces a GNN-based method for learning hierarchy-aware embeddings of knowledge graphs by incorporating a semantic loss derived from underlying ontologies. Applied to predicting cell-growth phenotypes from gene knockouts in yeast, it reports a mean R² of 0.360 over 10-fold CV (improving to 0.377 with semantic loss), demonstrates generalization to triple knockouts, generates interpretable hypotheses from the KG, and validates one hypothesis experimentally. It also explores box embeddings for KG revision evaluation.

Significance. If the results hold, the work provides evidence that ontology hierarchies can be exploited via semantic loss to improve quantitative predictions from GNN embeddings on biological data, with the experimental validation of a model-derived hypothesis and generalization to unseen triple knockouts as clear strengths. This approach could bridge qualitative domain knowledge with experimental outcomes in phenotype prediction and similar tasks.

major comments (2)
  1. [Abstract] Abstract: The central claim that semantic loss improves predictive performance (R² 0.360 to 0.377) by aligning embeddings with ontology structure lacks supporting per-fold R² values, standard deviations, or a statistical test for the 0.017 difference. Without these or an ablation replacing the ontology-derived loss with a non-hierarchical regularizer of comparable strength, it is unclear whether the gain stems from hierarchy alignment rather than generic extra gradient signal.
  2. [Abstract] Abstract and Methods: Baseline definitions, exact data splits for the 10-fold CV, and full details of yeast KG construction from community databases and ontology terms are not provided. These omissions are load-bearing for verifying the reported R² superiority and the assumption that the assembled KG encodes the biologically relevant relations for cell-growth phenotypes after gene deletions.
minor comments (1)
  1. The manuscript would benefit from clearer notation distinguishing the semantic loss formulation from standard GNN objectives, and from explicit statements of how box embeddings are learned in the absence of a prediction task.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and recommendations. We address each of the major comments in detail below, indicating the revisions we plan to make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that semantic loss improves predictive performance (R² 0.360 to 0.377) by aligning embeddings with ontology structure lacks supporting per-fold R² values, standard deviations, or a statistical test for the 0.017 difference. Without these or an ablation replacing the ontology-derived loss with a non-hierarchical regularizer of comparable strength, it is unclear whether the gain stems from hierarchy alignment rather than generic extra gradient signal.

    Authors: We agree that providing per-fold R² values, standard deviations, and a statistical test would better support the reported improvement. In the revised manuscript, we will include these details from our cross-validation experiments and conduct a paired t-test or similar to evaluate the significance of the 0.017 difference. For the ablation study, we acknowledge the value of comparing against a non-hierarchical regularizer to isolate the effect of the ontology-derived semantic loss. We will perform this additional experiment and report the results in the revised version. revision: yes

  2. Referee: [Abstract] Abstract and Methods: Baseline definitions, exact data splits for the 10-fold CV, and full details of yeast KG construction from community databases and ontology terms are not provided. These omissions are load-bearing for verifying the reported R² superiority and the assumption that the assembled KG encodes the biologically relevant relations for cell-growth phenotypes after gene deletions.

    Authors: We will expand the Methods section to include precise definitions of all baseline methods, the exact partitioning of data for the 10-fold cross-validation (including how folds were stratified if applicable), and a comprehensive description of the yeast knowledge graph construction process, specifying the community databases and ontology terms used along with any filtering or integration steps. This additional information will facilitate independent verification of our results and the biological relevance of the KG. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper constructs a yeast knowledge graph from external community databases and ontologies, trains GNNs with an ontology-derived semantic loss on a subset of gene-knockout data, and evaluates predictive R² on held-out experimental phenotypes via 10-fold cross-validation. The reported performance gains (0.360 to 0.377) are empirical outcomes on independent biological measurements rather than algebraic identities or reparameterizations of the fitted inputs; the semantic loss is defined from external hierarchy structure, not from the cell-growth targets themselves. No self-definitional equations, fitted-input-as-prediction reductions, or load-bearing self-citation chains appear in the derivation chain for the central claims.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central performance claims rest on the assumption that the assembled yeast KG faithfully encodes growth-relevant biology and that the semantic loss correctly encodes useful hierarchical constraints; no explicit free parameters or invented entities are named in the abstract.

axioms (1)
  • domain assumption Ontology class hierarchies provide biologically meaningful constraints that improve embedding quality for downstream phenotype prediction.
    Invoked when semantic loss is added to align embeddings with ontology structure.

pith-pipeline@v0.9.0 · 5837 in / 1220 out tokens · 49880 ms · 2026-05-21T08:16:52.026347+00:00 · methodology

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