Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Authors: Tianyu Liu, Yuge Wang, Rex Ying, Hongyu Zhao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive benchmarking analysis shows our model s capacity to effectively capture gene function similarity across multiple modalities, outperforming state-of-the-art methods in gene representation learning by up to 97.5%. Moreover, we employ bioinformatics tools in conjunction with gene representations to uncover pathway enrichment, regulation causal networks, and functions of disease-associated or dosage-sensitive genes. |
| Researcher Affiliation | Academia | Tianyu Liu Yale University EMAIL Yuge Wang Yale University EMAIL Rex Ying Yale University EMAIL Hongyu Zhao* Yale University EMAIL |
| Pseudocode | Yes | Algorithm 1 Multimodal Similarity Learning Graph Neural Network (Mu Se-GNN) |
| Open Source Code | Yes | 1Codes of Mu Se-GNN: https://github.com/Hello World LTY/Mu Se-GNN |
| Open Datasets | Yes | Leveraging 82 training datasets from 10 tissues, three sequencing techniques, and three species, we create informative graph structures for model training and gene representations generation... Download links: Appendix M. ... We used Mu Se-GNN to generate gene embeddings for different datasets based on an unsupervised learning framework and utilized the gene embeddings as training dataset to predict the function of genes based on k-NN classifier. |
| Dataset Splits | No | The paper does not explicitly state training/validation/test dataset splits with specific percentages or counts for reproducibility. It discusses evaluation metrics and benchmarking but not a dedicated validation split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run the experiments (e.g., specific GPU/CPU models, memory, or cloud resources). |
| Software Dependencies | No | The paper mentions software like "Scanpy [104]" and tools like "ggplot2 [102]" but does not provide specific version numbers for these or other key software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | Details of hyper-parameter tuning can be found in Appendix E.2. |