Gene Regulatory Network Inference as Relaxed Graph Matching
Authors: Deborah Weighill, Marouen Ben Guebila, Camila Lopes-Ramos, Kimberly Glass, John Quackenbush, John Platig, Rebekka Burkholz10263-10272
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. ... Experiments on Synthetic Data ... Experiments on Cancer Data ... Table 1: TF binding prediction for different cancer tissues. |
| Researcher Affiliation | Academia | 1 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 2 Channing Division of Network Medicine, Brigham and Women s Hospital, 3 Harvard Medical School, Boston, MA 02115 |
| Pseudocode | No | The paper describes algorithms but does not provide pseudocode or algorithm blocks. It refers to detailed algorithms in the supplement. |
| Open Source Code | Yes | OTTER is available in R, Python, and MATLAB through the net Zoo packages: net Zoo R v0.7 (https://github. com/net Zoo/net Zoo R), net Zoo Py v0.7 (https://github.com/ net Zoo/net Zoo Py), and net Zoo M v0.5 (https://github.com/ net Zoo/net Zoo M). We provide a tutorial to walk the users through the usage of OTTER in R (https://netzoo.github.io/net Zoo R/). |
| Open Datasets | Yes | We obtained bulk RNA-seq data from the Cancer Genome Atlas (TCGA) (Tomczak, Czerwi nska, and Wiznerowicz 2015). The data is downloaded from recount2 (Collado-Torres et al. 2017). |
| Dataset Splits | Yes | Breast and cervix data serve therefore as training data while the liver cancer data is an independent test set. Hyperparameter tuning of OTTER was assisted by MATLAB s bayesopt function utilizing a Gaussian process prior to maximize the joint AUC-PR for breast and cervix cancer, max AUPRbreast AUPRcervix. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models). |
| Software Dependencies | No | The paper mentions using the 'ADAM method (Kingma and Ba 2014)' and 'MATLAB s bayesopt function' but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | ADAM gradient descent for the OTTER objective is run for 10^4 steps with the default ADAM parameters, as detailed in the supplement. For both the gradient decent and the spectral approach, we use parameters γ = σ^2 p/c = σ^2 p = σ^2 c and λ = 0.5. Hyperparameter tuning of OTTER was assisted by MATLAB s bayesopt function utilizing a Gaussian process prior to maximize the joint AUC-PR for breast and cervix cancer, max AUPRbreast AUPRcervix. |