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..
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Authors: Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically test the effectiveness of our method in denoising a network by demonstrating an improvement in community detection results on multi-resolution Hi-C data both with and without Capture-C-generated partial labels. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University 2Department of Electrical Engineering, Stanford University 3Department of Genetics, Stanford University 4Department of Physics, Stanford University |
| Pseudocode | No | The paper describes the optimization steps and variable updates mathematically and textually, but does not include structured pseudocode or an explicitly labeled algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of its source code, nor does it include a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | For the real data, we started with a ground truth of domains previously identified in the GM12878 cell line chromosomes 14 and 21 [10] |
| Dataset Splits | No | The paper describes the validation methodology (checking features of domains), but it does not specify concrete training/validation dataset splits (e.g., percentages, sample counts, or cross-validation folds) needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'standard numerical toolboxes in MATLAB' but does not provide specific version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | No | The paper mentions tuning parameters (λ, β, γ) and refers to 'Appendix 7.3' for details; however, the specific values for these parameters are not provided within the main text of the paper. |