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 | Conference PDF | Archive PDF | Plain Text | 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. |