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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Authors: Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, RISHITA ANUBHAI
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results demonstrate that Bio BRIDGE can beat the best baseline KG embedding methods (on average by 76.3%) in cross-modal retrieval tasks. |
| Researcher Affiliation | Collaboration | Zifeng Wang University of Illinois Urbana-Champaign EMAIL Zichen Wang Amazon AWS AI EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Code is at https://github.com/Ryan Wang Zf/Bio Bridge. |
| Open Datasets | Yes | We draw a subset of Prime KG (Chandak et al., 2023) to build the training knowledge graph. |
| Dataset Splits | Yes | For each type of triple, we randomly sample 80%, 10%, and 10% for the train, validation, and test sets, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments, only mentioning general experimental setup. |
| Software Dependencies | No | The paper mentions specific models like ESM2-3B, Uni Mol, and Pub Med BERT, but does not provide version numbers for ancillary software dependencies such as deep learning frameworks or specific libraries. |
| Experiment Setup | Yes | As such, we keep the same set of hyperparameters for Bio Bridge across all experiments: batch size 4096, training epochs 50, and learning rate 1e-4. |