BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Authors: Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, RISHITA ANUBHAI
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 zifengw2@illinois.edu Zichen Wang Amazon AWS AI zichewan@amazon.com |
| 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. |