GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks
Authors: Angelina Brilliantova, Hannah Miller, Ivona Bezáková
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Empirical Evaluation Data Collection and Setup Synthetic datasets To validate that our algorithms can correctly identify the GRASMOS parameters, we created synthetic datasets with known Θ, resembling real-world GRNs by their network parameters and size. |
| Researcher Affiliation | Academia | Angelina Brilliantova, Hannah Miller, Ivona Bez akov a Rochester Institute of Technology, 102 Lomb Memorial Drive, Rochester, NY 14623 lb9849@rit.edu, hm@mail.rit.edu, ib@cs.rit.edu |
| Pseudocode | No | The paper describes algorithmic approaches but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/Restel/grasmos |
| Open Datasets | Yes | We used two public databases: Regulon DB (Santos-Zavaleta et al. 2019) and Subti Wiki (Pedreira, Elfmann, and St ulke 2022; Fl orez et al. 2009). |
| Dataset Splits | No | The paper refers to using 'synthetic datasets' and 'real-world GRN datasets E.coli and B.subtilis' for evaluation. However, it does not explicitly specify exact training, validation, and test splits (e.g., percentages or sample counts) for these datasets. |
| Hardware Specification | Yes | We estimated L(Θ) of the BNC parameters using a parallel implementation on the RIT research computing cluster. We used up to 432 nodes with each core estimating a likelihood of a single Θ-candidate via MCMC sampling. Each core is equipped with Intel Xeon Gold 6150 CPU @ 2.70GHz. The RAM upper limit for our computation was 2048 MB. |
| Software Dependencies | No | The paper describes algorithmic approaches and computational setup but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters within the Θ-candidates varied between 0.1 and 0.9, with an increment step of 0.1. We tested 4 different Θ-generators in the generation part and for each of 1458 of Θ-candidates in the reconstruction part. For fitting the GRASMOS parameters of real-world GRNs, we: used a fine-grained exploration of parameters varying in [0.1, 0.9] with an increment of 0.05 belonging to the SC, TC, and NO models; and, for complexity reasons, we evaluated the BNC model subspace using a coarse-grid with each parameter varying in {0.25, 0.5, 0.75}. |