Welcome to BioMedScribe, a repository containing various projects centered around the statistical analysis of medical data and multiomics modeling. This README provides an overview of the contents and purpose of each directory within the repository.
The multiomics directory contains analysis code centralized around the research in "Multiomics modeling of the immunome, transcriptome, microbiome, proteome, and metabolome adaptations during human pregnancy" by Ghaemi et al. (2019). The focus of this research is to explore the interactions and adaptations of multiple omics data types during pregnancy. In this directory, you will find machine learning (supervised regression) and deep learning methods used to model and predict the target outcome. The initial version of this codebase was created as a submission for a PhD position application.
The myocardial-infarctions directory contains statistical analysis of myocardial infarction complications. The goal of this analysis is to identify the importance hierarchy of predictor variables from the date of admission to the end of the 3rd day. The code and results in this directory originated from a lab work submission during my Erasmus semester studies at Trinity College Dublin.
The repository is structured as follows:
multiomics/: Contains the code and resources for the multiomics modeling project.myocardial-infarctions/: Includes the code and analysis results for the study on myocardial infarction complications.
Feel free to explore each directory for more detailed information and access the code and data.
To use the code in this repository, follow these steps:
-
Clone the repository:
git clone https://github.com/nishan-chatterjee/BioMedScribe.git
-
Navigate to the desired directory:
cd BioMedScribe/multiomics/or
cd BioMedScribe/myocardial-infarctions/ -
Follow the instructions provided in each directory's README to reproduce the analysis or use the code for your own research.
Contributions to this repository are welcome. If you would like to contribute, please open an issue to discuss the proposed changes or submit a pull request with your contributions.
This repository is licensed under the MIT License.