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 [1].
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Authors: Yulun Wu, Rob Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno, Layne C Price, Luis F. Voloch, George Karypis
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With extensive experiments, we exhibited the advantage of our approach over state-of-the-art deep learning models for individual response prediction. |
| Researcher Affiliation | Collaboration | Yulun Wu University of California, Berkeley yulun EMAIL Robert A. Barton Immunai EMAIL Zichen Wang Amazon EMAIL Vassilis N. Ioannidis Amazon EMAIL Carlo De Donno Immunai EMAIL Layne C. Price Amazon EMAIL Luis F. Voloch Immunai EMAIL George Karypis Amazon EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | More details regarding hyperparameter settings can be found in our code repository . https://github.com/yulun-rayn/graph VCI |
| Open Datasets | Yes | We employ the publicly available sci Plex dataset from Srivatsan et al. (2020) (Sciplex) and CRISPRa dataset from Schmidt et al. (2022) (Marson). In addition, we open source in this work a new dataset (L008) designed to showcase the power of our model in conjunction with modern genomics. |
| Dataset Splits | Yes | The data excluding the OOD set are split into training and validation set with a four-to-one ratio. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or specialized packages used for the experiments. |
| Experiment Setup | No | All common hyperparameters of all models (network width, network depth, learning rate, decay rate, etc.) are set to the same as Lotfollahi et al. (2021a). More details regarding hyperparameter settings can be found in our code repository . |