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..
Biologically Plausible Brain Graph Transformer
Authors: Ciyuan Peng, Yuelong Huang, Qichao Dong, Shuo Yu, Feng Xia, Chengqi Zhang, Yaochu Jin
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three benchmark datasets demonstrate that Bio BGT outperforms state-of-the-art models, enhancing biologically plausible brain graph representations for various brain graph analytical tasks. |
| Researcher Affiliation | Academia | 1Federation University Australia, 2Dalian University of Technology, 3Zhejiang Gongshang University, 4RMIT University, 5Hong Kong Polytechnic University, 6Westlake University |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and descriptive text, such as in Sections 3.1 and 3.2, but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/pcyyyy/BioBGT. |
| Open Datasets | Yes | Datasets. We conduct experiments on f MRI data collected from three benchmark datasets. (1) Autism Brain Imaging Data Exchange (ABIDE) 3 dataset. This dataset contains resting-state f MRI data of 1, 009 anonymous subjects... 3https://fcon_1000.projects.nitrc.org/indi/abide/ (2) Alzheimer s Disease Neuroimaging Initiative (ADNI) 4 dataset... 4https://adni.loni.usc.edu/ (3) Attention Deficit Hyperactivity Disorder (ADHD-200) 5 dataset... 5https://fcon_1000.projects.nitrc.org/indi/adhd200/ |
| Dataset Splits | Yes | Each dataset is randomly split, with 80% used for training, 10% for validation, and 10% for testing. |
| Hardware Specification | Yes | Model training is performed on an NVIDIA A6000 GPU with 48GB of memory. |
| Software Dependencies | Yes | Our model is implemented using PyTorch Geometric v2.0.4 and PyTorch v1.9.1. |
| Experiment Setup | Yes | The detailed hyperparameter settings for training Bio BGT on three datasets are summarized in Table 3. (Table 3 lists: #Layers 3, #Attention heads 8, Threshold of edge weight 0.3 0 0, Hidden dimensions 128, FFN hidden dimensions 256, Dropout 0.5 0.1 0.1, Readout method mean, Learning rate 3e-4, Batch size 128, #Epochs 200, Weight decay 1e-4, Warm-up Steps 10) |