Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
Authors: Ronald Xie, Kuan Pang, Sai Chung, Catia Perciani, Sonya MacParland, Bo Wang, Gary Bader
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate BLEEP s effectiveness in gene expression prediction by benchmarking its performance on a human liver tissue dataset captured using the 10x Visium platform, where it achieves significant improvements over existing methods. |
| Researcher Affiliation | Academia | 1University of Toronto, 2Vector Institute, 3University Health Network, 4The Donnelly Centre, 5Toronto General Hospital Research Institute, 6Canadian Institute for Advanced Research (CIFAR) |
| Pseudocode | Yes | Algorithm 1 Bimodal Embedding for Expression Prediction |
| Open Source Code | Yes | Code available at https://github.com/bowang-lab/BLEEP |
| Open Datasets | Yes | The dataset [2, 3] used to train and benchmark BLEEP... The data is publicly available for download at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE240429. |
| Dataset Splits | No | The paper mentions holding out one slice for testing and using the others for training, but it does not explicitly state specific percentages or methods for a separate validation split within the main text. |
| Hardware Specification | Yes | BLEEP is trained using 4 NVIDIA V100 GPUs with the Adam W optimizer[12], a batch size of 512 and a learning rate of 0.001 for 150 epochs. |
| Software Dependencies | No | The paper mentions software like "Scanpy package" and "Harmony", but it does not provide specific version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | BLEEP is trained using 4 NVIDIA V100 GPUs with the Adam W optimizer[12], a batch size of 512 and a learning rate of 0.001 for 150 epochs. |