Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors propose a sparse denoising autoencoder that extracts gene representations from large-scale population-level genotype data, which can be used to identify gene-to-disease associations. The autoencoder connections are modeled on a bipartite biological knowledge graph that connects individual variants to nearby genes. 
strengths_and_weaknesses: The graph input enables each node in the hidden layer to be used as an interpretable, multi-purpose gene embedding based on information from close variants most likely to impact gene function. The proposed model uses knowledge graphs (already proposed) in an autoencoder to extract multi-purpose embeddings of genes that can then be used in downstream applications. It also enables gene embeddings to be derived independently for each gene or gene set of interest and decouples the feature extraction and downstream tasks, achieving lower computational costs. The proposed model and experimental setup are clear and reproducible and the achieved results seem significant/interesting.

suggested_changes: Some main results from experiments (gene-disease associations/disease prediction) should be in the main text (currently all results are in the Appendix).
Would be interesting to include comparisons with other benchmark models.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper introduces GeneDAE a sparse denoising autoencoder which uses an adjacency matrix to constrain the connectivity of the encodings and decodings in a model on variant-to-gene annotations connecting variants to nearby genes. This concept is used to extract multi-purpose embeddings of genes that can be used in downstream applications.
strengths_and_weaknesses: **Strengths**

- The paper is well written.  The method is concise and easy to follow.
- The paper shows the ability to extract interpretable gene embeddings from population-level genotype variant data, offering a potentially useful tool for the discovery or analysis of gene-to-disease associations in genomics research.
- The presented results suggest a significant association between the HSPA1B gene and T1D

**Weaknesses**

- While it seems to work well, the paper does not explain the rationale behind some of the choices made. Can this method be used in other model types such as Graph Models, considering the use of adjacency matrixes?
- There is no comparison with other methods, if any. This will be important to further validate the paper's claims.
- There is no information about the reproducibility of the results.
suggested_changes: - The authors should include information about reproducibility in the paper.
review_title: null

Meta-review: metareview: Good paper with all reviewers arguing for acceptance. It could be better if suggestions from reviewers could be incorporated into the manuscript.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present