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..
Large-Scale Differentiable Causal Discovery of Factor Graphs
Authors: Romain Lopez, Jan-Christian Huetter, Jonathan Pritchard, Aviv Regev
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | DCD-FG uses a Gaussian non-linear low-rank structural equation model and shows significant improvements compared to state-of-the-art methods in both simulations as well as a recent large-scale single-cell RNA sequencing data set with hundreds of genetic interventions. |
| Researcher Affiliation | Collaboration | 1 Division of Research and Early Development, Genentech EMAIL 2 Department of Genetics, Stanford University EMAIL 3 Department of Biology, Stanford University |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] (Appendix) |
| Open Datasets | Yes | We focus on a recent Perturb-CITE-seq experiment [14] that contains expression profiles from 218,331 melanoma (cancer) cells, after interventions targeting each of 249 genes. and the citation: [14] Chris J Frangieh, Johannes C Melms, Pratiksha I Thakore, Kathryn R Geiger-Schuller, Patricia Ho, Adrienne M Luoma, Brian Cleary, Livnat Jerby-Arnon, Shruti Malu, Michael S Cuoco, et al. Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nature Genetics, 53(3):332 341, 2021. |
| Dataset Splits | Yes | For every model, we performed a hyperparameter search using a goodness of fit metric on a small validation set. |
| Hardware Specification | Yes | on one NVIDIA Tesla T4 GPU with 15Gb of RAM |
| Software Dependencies | No | The paper mentions 'Python 3.8' and packages like 'PyTorch and SciPy' and 'Scanpy [63]' but does not provide specific version numbers for the libraries/packages used. |
| Experiment Setup | No | We provide further details on all experiments, including the grids used for hyperparameter search, as well as supplementary experiments, in Appendix F. |