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..
Multimodal Prototyping for cancer survival prediction
Authors: Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag Jayant Vaidya, Alexander Baras, Faisal Mahmood
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluation on six cancer types shows that our framework outperforms state-of-the-art methods with much less computation while unlocking new interpretability analyses. |
| Researcher Affiliation | Academia | 1Mass General Brigham, Boston, MA, USA. 2Harvard Medical School, Boston, MA, USA. 3Massachusetts Institute of Technology, Cambridge, MA, USA. 4Johns Hopkins University School of Medicine, Baltimore, MD, USA. |
| Pseudocode | No | The paper provides detailed explanations and mathematical derivations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https: //github.com/mahmoodlab/MMP. |
| Open Datasets | Yes | We use publicly available The Cancer Genome Atlas (TCGA) to evaluate MMP across six cancer types: Bladder urothelial carcinoma (BLCA) (n = 359), Breast invasive carcinoma (BRCA) (n = 868), Lung adenocarcinoma (LUAD) (n = 412), Stomach adenocarcinoma (STAD) (n = 318), Colon and Rectum adenocarcinoma (CRC) (n = 296), and Kidney renal clear cell carcinoma (KIRC) (n = 340). ... Log-2 transformed transcripts per million bulk RNA sequencing expression for all TCGA cohorts is accessed through UCSC Xena database (Goldman et al., 2020). |
| Dataset Splits | Yes | Following standard practice, we use 5-fold site-stratified cross-validation to mitigate batch effect (Howard et al., 2021). |
| Hardware Specification | No | The paper does not explicitly state the specific hardware (e.g., GPU models, CPU types, or memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions optimizers (Adam W) and libraries in citations (e.g., scikit-survival), but does not provide a reproducible description of ancillary software with specific version numbers for key components like programming languages or deep learning frameworks. |
| Experiment Setup | Yes | All models are trained with a 1 10 4 learning rate with cosine decay scheduler, Adam W optimizer, and 1 10 5 weight decay for 20 epochs. MMP uses the Cox loss with a batch size of 64. |