GENRAD: Genomics and Radiomics Heterogeneous Graph Neural Network for Graph-Level Classification in Alzheimer's Disease

Authors: Salma Hassan, Mostafa Salem, Mohammad Yaqub
Primary area: learning on graphs and other geometries & topologies
Keywords: Heterogeneous Graph Neural Network (GNN), Alzheimer's Disease, Multimodal data fusion, Genomics, Radiomics, Node-level classification, Multi-scale graph representation, Explainable AI

Abstract:
Alzheimer’s Disease (AD) poses multifaceted challenges due to its neurodegenerative nature driven by complex genomic, radiomic, and structural interactions. Understanding these complex relationships is pivotal for advancing diagnostic and therapeutic approaches. Current models struggle to effectively integrate multimodal data for AD, limiting their predictive accuracy and biological interpretability. Thus, there is a pressing need for models that can seamlessly fuse genomic and radiomic data to provide a holistic understanding of AD pathology. We introduce GENRAD, a novel heterogeneous graph neural network (GNN) that integrates multimodal genomic and radiomic data for graph-level classification in AD by representing patients, genes, and brain structures as distinct nodes and implementing advanced message-passing techniques. The benefits of GENRAD are fourfold: (1) It enables multimodal fusion of genomic and radiomic data, uncovering biologically meaningful insights missed by single-modality models. (2) Its adaptive multi-scale graph representations model interactions at various biological scales, capturing complex relationships essential for understanding AD pathology. (3) GENRAD incorporates explainable AI techniques, providing detailed analysis of key genomic markers and brain regions associated with AD. (4) GENRAD performs unsupervised clustering of genes, allowing the identification of functionally related biological pathways, thus empowering clinicians with actionable insights for personalized treatment strategies. GENRAD demonstrates superior classification accuracy in identifying AD-related patterns compared to existing machine and deep learning models, achieving an accuracy of 91.70%.

Decision: reject
Reviewer scores: 3, 3, 3, 5

Reviews and discussion:
<thread>
creation date:
1729708060030

thread_type:
official_review

<thread_object>
creator: reviewer_24i2

<content>
summary:
This paper proposes a heterogeneous GNN that integrates multimodal genomic and radiomic data for graph-level classification in Alzheimer’s Disease. The method represents patients, genes, and brain structures as distinct nodes and employs advanced message-passing techniques to improve classification performance.

strengths:
1. The idea of incorporating neuroimaging data with demographic and genetic information is well-founded and addresses a relevant need in AD research.
2. The experimental results are impressive, showing strong performance on ANMerge dataset.

weaknesses:
1. **Questionable Radiomic Feature Construction**: The choice of radiomic feature construction is unclear. Numerous established brain atlases, both anatomical [1] and functional [2], have been widely used in neuroscience and GNN research [3, 4] to construct brain networks. It is not explained why the authors opted for a segmentation model instead of utilizing these well-established atlases to generate structural data. Additionally, using 3D Euclidean distance to construct structure-structure edges seems questionable. Physical proximity between regions does not necessarily imply similar behavior or co-activation. The common practice in functional brain network construction typically involves using correlation metrics, not physical distance, to define edges.

2. **Limited Literature Discussion**: The paper lacks sufficient discussion on prior work that incorporates MRI and demographic data using GNNs. Existing works [5-8] in this area should be acknowledged and incorporated into both the discussion and experimental comparisons to contextualize the contribution of the proposed method.

3. **Missing Data Statistics**: Key data statistics, such as the number of patients, the distribution of classes, age and gender are missing. This information is crucial for transparency and reproducibility and should be included in the paper.

4. **Presentation Issues**: The presentation could be improved for better clarity:
   - (1) The figure on page 5 lacks a caption.
   - (2) In the first sentence of Section 3.2.3, "Figure 2b" should refer to "Figure 2a."
   - (3) The text and edges in Figure 3 are difficult to read and should be made clearer.
   - (4) I suggest revising Table 2 by separating binary classification results from multi-class classification results, as these settings are not directly comparable.

[1] Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage 2002

[2] Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri. Cerebral cortex 2018

[3] Data-driven network neuroscience: On data collection and benchmark. NeurIPS 2023

[4] NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics. NeurIPS 2023

[5] Self-attention equipped graph convolutions for disease prediction. ISBI 2019

[6] Multicenter and multichannel pooling GCN for early AD diagnosis based on dual-modality fused brain network. TMI 2022

[7] Multi-modal graph learning for disease prediction. TMI 2022

[8] DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. Translational Psychiatry, 2024

questions:
Please refer to weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1730214533009

thread_type:
official_review

<thread_object>
creator: reviewer_rnjg

<content>
summary:
This paper introduces GENRAD, a novel heterogeneous graph neural network that improves Alzheimer's Disease classification by integrating genomic and radiomics data through a graph structure representing patients, genes, and brain regions as nodes. The model achieves 91.70% classification accuracy while providing interpretable insights about disease pathology through explainable AI techniques and unsupervised gene clustering, advancing both diagnostic capabilities and potential treatment strategies.

strengths:
Building heterogeneous graph from radiomic and genomic information is novel, based on the reviewer's knowledge.

The paper is clear and easy to follow.

Thorough ablation study has been provided.

Potentially new findings can be found from the interpretations.

weaknesses:
The comparisons in Table 1 are not convincing. For example, to demonstrate that GENRAD could outperform Zheng et al (2018) and Maddalena et al (2022), the baseline models should be retrained on the ANMerge. It is not fair to just put their results on ADNI in the table.

It is not clear how authors defined the edge between patient node and structure/gene node. And why there is no direct interaction between structure and gene node? This is important for AD for several reasons, like the amyloid and tau can be accumulated in different regions with different extents. Besides, the structure-structure edges were built based on 3D Euclidean distance between different regions. Is this better than other ways like covariance matrix from cortical thickness, or the correlation between node of brain regions, etc?

Why SAGEConv was chosen? There are many, many other message passing layers out there that are feasible, which should be compared with.

Neither code nor data is provided, make it hard to evaluate the reproducibility. It could be beneficial for the cummunity if they can be shared, though it is not necessary at this stage.

Minor: Missing index and caption for figure 2.

questions:
Please refer to the weakness.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730442076821

thread_type:
official_review

<thread_object>
creator: reviewer_oyxn

<content>
summary:
This paper introduces GENRAD, a heterogeneous graph neural network designed to integrate genomic and radiomic data for Alzheimer’s Disease (AD) classification. GENRAD is structured to handle multimodal data, representing patients, genes, and brain structures as distinct nodes and using advanced message-passing techniques. It claims four major contributions: enhancing multimodal data fusion, creating adaptive multi-scale graph representations, improving explainability, and enabling unsupervised clustering of genes to identify biologically relevant pathways. The model demonstrates superior accuracy compared to other methods in classifying AD.

strengths:
1. The paper is well-structured and easy to understand.
2. The paper provides visualization results that highlight key genomic markers and brain regions associated with Alzheimer’s Disease (AD).

weaknesses:
1. Limited Technical Contribution. To handle the multi-modality data integrating genomic and radiomic information, this paper employs a message-passing technique originally proposed in [1] on a heterogeneous graph that models patients, genes, and brain structures with both local and global interactions. This existing approach constitutes the paper’s major and only technical contribution, making the overall contribution marginal.

[1] Hamilton, W., Ying, Z. and Leskovec, J., 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, 30.

2. Incomplete Literature Review. This paper lacks a comprehensive overview of existing multimodal data fusion methods, making it unclear how GENRAD specifically advances this field. The following papers are highly relevant to this study.

[2] Gaiteri, C., Ding, Y., French, B., Tseng, G. C. and Sibille, E., 2014. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes, brain and behavior, 13(1), pp.13-24.

[3] Gaiteri, C., Mostafavi, S., Honey, C. J., De Jager, P. L. and Bennett, D. A., 2016. Genetic variants in Alzheimer disease—molecular and brain network approaches. Nature Reviews Neurology, 12(7), pp.413-427.

[4] Bodalal, Z., Trebeschi, S., Nguyen-Kim, T. D. L., Schats, W. and Beets-Tan, R., 2019. Radiogenomics: bridging imaging and genomics. Abdominal radiology, 44(6), pp.1960-1984.

[5] Li, S. and Zhou, B., 2022. A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiation Oncology, 17(1), p.217.

[6] Wang, M., Roussos, P., McKenzie, A., Zhou, X., Kajiwara, Y., Brennand, K. J. and Zhang, B., 2016. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome medicine, 8, 1-21.

[7] Singh, G., Manjila, S., Sakla, N., True, A., Wardeh, A. H., Beig, N. and Spektor, V., 2021. Radiomics and radiogenomics in gliomas: a contemporary update. British journal of cancer, 125(5), pp.641-657.

3. Lack of Justification for Methodological Choices. The rationale behind certain technical decisions—such as using the SAGEConv layer for message-passing—lacks detailed justification specific to the properties of the multimodal data. Furthermore, the reasons for employing the GeneMANIA method to obtain co-expression scores and for defining edges between brain structures based on 3D Euclidean distance remain unclear.

4. Insufficient Evidence and Contextual Interpretation. On page 7, it is stated that GENRAD incorporates explainable AI techniques, such as GNNExplainer, alongside biological interpretability frameworks to make predictions that are not only accurate but also clinically meaningful. However, GNNExplainer is neither discussed in the methodology section nor tailored to the specific properties of multimodal data, which leaves the AI techniques used insufficiently explained. Additionally, while visualizations of gene interactions and brain regions affected by Alzheimer’s Disease (AD) are provided, they lack biological context and supporting evidence, raising questions about the relevance of the identified biomarkers and brain regions and their alignment with established neuroscience findings.

5. Limited Dataset. The model evaluation uses a single dataset (ANMerge), limiting the generalizability of the results. Moreover, the dataset details, including size and data distribution, are not fully described.

6. Comparative Analysis with Broader Baselines. To validate GENRAD’s performance further, comparisons with a more comprehensive set of baselines, particularly from recent transformer-based approaches, would strengthen the findings.

7. Marginal Performance Improvement. The classification results in Table 1 show that the proposed GENRAD achieves only a slight improvement over the follow-up methods and, in some cases, performs even worse.

8. Unclear Presentation. Numerous abbreviations are used without providing their full terms, and Figure 2 lacks a caption, affecting overall clarity.

questions:
1. Could the authors elaborate on why SAGEConv was chosen over other potential GNN architectures? How does it specifically benefit the integration of genomic and radiomic data?

2. Given the single dataset used, how do the authors view GENRAD’s applicability to other AD datasets, and are there plans for further validation with more diverse data sources?

3. While explainable AI techniques are included, how do the authors envision clinicians using these insights in real-world applications? Providing case studies or examples could be beneficial.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730584427295

thread_type:
official_review

<thread_object>
creator: reviewer_jzwm

<content>
summary:
The paper introduces GENRAD, a heterogeneous graph neural network (GNN) designed for Alzheimer’s Disease (AD) classification through the integration of genomic and radiomic data. This model represents patients, genes, and brain structures as nodes, allowing complex interactions to be analyzed using advanced message-passing techniques.

strengths:
1. The model’s use of multi-scale graph representations captures local and global biological interactions, providing a holistic understanding of AD pathology.
2. The unsupervised clustering of genes within the model facilitates the identification of functional biological pathways, which could aid in personalized treatment strategies.

weaknesses:
1.  The paper lacks a thorough discussion of related interpretable GNNs specifically designed for AD. Many studies have developed explainable GNNs for AD, such as those in [1, 2, 3, 4]. A comparison of these models with GENRAD in terms of both design and interpretability effectiveness is necessary to justify the interpretability of the proposed model.

[1] Li, Xiaoxiao, et al. "Braingnn: Interpretable brain graph neural network for fmri analysis." Medical Image Analysis 74 (2021): 102233.
[2] Kim, Mansu, et al. "Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data." 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021.
[3] Zhou, Houliang, et al. "Interpretable graph convolutional network of multi-modality brain imaging for alzheimer’s disease diagnosis." 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022.
[4] Xiao, Tingsong, et al. "Dual-graph learning convolutional networks for interpretable alzheimer’s disease diagnosis." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022.

2. While the paper claims computational efficiency, it does not provide theoretical or experimental evidence to substantiate this claim. A computational analysis section, including runtime comparisons, would justify the argument.

3. The paper does not provide a clear problem definition, which makes it hard to follow.

4. The mathematical equations contain typos and undefined symbols. For example, there is a mathematical symbol error in Equation (3) $\mathcal{N} _{\phi}\left( i \right)$ and undefined dimensions for variables.

5. From the Table 1, the performance of proposed methods is limited compared with other baselines, such as FGCNN, especially no standard deviation are provided.

questions:
1. How does GENRAD compare in interpretability and model design with other interpretable GNNs?

2. Could the author analyse the interpretable experimental results in medical domain perspective?

3. Could the author provide a detailed computational efficiency analysis, both theoretical and experimental, to support the efficiency claims?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732283667972

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Manuscript Revision

comment:
We would like to express our gratitude for the constructive feedback received from the reviewers, which has significantly improved the clarity and quality of our manuscript. Below, we outline the key revisions made and provide justifications for each change based on the reviewers' specific comments:

**Justification for Revisions**
**1. Splitting Table 1:** Based on feedback regarding the clarity of performance comparisons (e.g., W#7 from Reviewer RnJG), we moved the ADNI dataset details to the appendix to focus Table 1 exclusively on ANMerge results, avoiding confusion about baselines. As the old table created an unequal comparison that may not accurately reflect the strengths of GENRAD on ANMerge. By doing so, we aim to provide a fairer and more scientifically robust comparison, aligning the evaluation with the dataset-specific objectives of our study.

**2. Standard Deviation in Table 1:** Following the suggestion from Reviewer JZWM (W#5) to include standard deviation values for performance metrics, we added these to improve clarity and robustness in reporting.

**3. Dataset Class Split in Appendix:** To address concerns about missing dataset details (e.g., W#3 from Reviewer 24i2 and W#5 from Reviewer oYXN), we included class distribution and demographic statistics in the appendix for transparency and reproducibility.

**4. Message Passing Results in Appendix:** Reviewer oYXN (W#3) and Reviewer RnJG (W#3) questioned using SAGEConv. We added results from experimenting with alternative message-passing methods in the appendix, highlighting the empirical rationale for our choice.

**5. Computational Performance Results:** As suggested by Reviewer JZWM (W#2), we included computational performance analysis in the manuscript to substantiate claims of efficiency.

**6. Expanded Literature Review:** Reviewers JZWM (W#1), oYXN (W#2), and 24i2 (W#2) highlighted gaps in the literature discussion. We addressed this by incorporating comparative analyses with prior studies on interpretable GNNs and multimodal data integration.

**7. Edited Table 2:** Following Reviewer 24i2 (W#4), we separated binary and multi-class classification results for better comparability and readability. This change allows readers to better interpret the results within the appropriate task-specific context.

**8. Clarified Figure 2 Caption:** Reviewer RnJG and Reviewer 24i2 (W#4) noted ambiguities in Figure 2. We corrected the caption and ensured all abbreviations were defined for clarity.

**9. Updated Equation 3:** Reviewer JZWM (W#4) raised concerns about undefined dimensions and symbols. We revised Equation 3 to include a concrete example of modalities, improving mathematical clarity and contextual understanding.

We hope these updates comprehensively address the reviewers' feedback and enhance our work's overall quality and transparency. Thank you for considering our revised manuscript.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper presents a method based on graph neural networks. The proposed model incorporates different data sources (here: genomics and radiomics data) to improve predictive performance on the ANMerge dataset. While the paper has its strengths, in particular the idea of combining different modalities (which in turn may also serve to improve explainability), the current manuscript suffers from several shortcomings, to wit:

- Lack of strong technical innovation: While the paper relies on established methods—and there is nothing wrong with that—an ICLR submission typical requires some additional insights into either the models, the representations, or the analyses. Beyond the predictive performance benefits on one dataset, no such insights are reported. Even the interpretability aspects, explicitly mentioned as a relevant feature of the method, relies on existing methods. Beyond the modelling decisions (that is, the graph creation), some of which have been criticised by reviewers (please see below), the task is interchangeable in the sense that [GNNExplainer](https://arxiv.org/abs/1903.03894) could be applied to other types of data and other tasks.

- Lack of additional comparison partners and datasets: Additional datasets would be helpful in assessing the claims of the paper; the reviewers also cited additional comparison partners that should be discussed (please see below). It should be noted that this is not the standard argument of reviewers and ACs giving authors a hard time by requiring more datasets. Rather, given the high relevance of the topic, an assessment of the generalisation performance (for instance) or the performance on a large-scale dataset would be highly useful. From the text, the well-established [ADNI dataset](https://adni.loni.usc.edu/) is also mentioned, but the training process on said dataset remains unclear.

- Lack of accessibility: The submission also suffers from accessibility and readability issues, mixing descriptions of a general model with specific implementations. There are numerous choices for the resulting model that are not justified (or ablated), for instance the selection of brain regions, which are [well-known to influence the results of a study](https://www.sciencedirect.com/science/article/pii/S1053811907011020).

It is for these reasons that a major revision, followed by another round of reviews, is required to fairly assess this paper. I therefore have to suggest rejecting this paper for now. I realise that this is not the desired outcome for the authors, so I hope that the discussion phase can be used to address some of the remaining issues for a resubmission. To add a suggestion to the authors: If this work were to be sent to a neurology venue and additional details/analyses were to be added, this has the potential to be a strong contribution to the literature. As it stands now, it will be hard to assess to what extent the performance improvements are suitable for any downstream applications. I understand that this is partially the fate of any paper attempting to bring ML into the applications, but I would strongly suggest the authors to either invest in a more detailed analyses of the results, or extend their paper with theoretical/model insights that pertain to AD.