Modeling and Predicting Neuronal Network Behavior Using Gene Expression Data
Table of Contents
This project aims to model and predict neuronal network behavior by analyzing the gene expression of individual neurons, characterized by their anatomical and functional properties. The dataset used for this project can be found here.
Research Question: "How can the transcriptional profiles of individual neurons, characterized by their anatomical and functional properties, be used to model and predict neuronal network behavior in a neurosymbolic framework?"
- R 4.3.1
- RStudio 2023.06.0+421
- BiocManager
- Clone the repository to your local machine.
- Install the required dependencies.
- Download the dataset and place it in the appropriate directory.
Detailed instructions on how to run the code and use the dataset will be provided.
- Data Preprocessing and Exploration
- Feature Engineering
- Model Training and Evaluation
- Interpretation and Analysis
Distributed under the MIT License. See LICENSE for more information.
Ethan Fan - Github
Matheus Kunzler Maldaner - Github
Nathan Gilman - Github
Rama Janco - Github
- Kiley Graim for instructing the course.
- Pfeffer CK and Beltramo R for authoring the original research and dataset.
- The Sequence Read Archive (SRA) for hosting the source repository.
- refine.bio and the Childhood Cancer Data Lab (CCDL) for providing a uniformly processed and normalized version of the dataset.
- Alex's Lemonade Stand Foundation for powering the refine.bio project.
- Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler for their contributions to refine.bio.