Develop an open-source tool that analyzes the relationship between various hormones (including but not limited to thyroid hormones, cortisol, estrogen, progesterone, and testosterone) and mood disorders, with the potential for future expansion into other areas such as neurodevelopment and signaling pathways.
Innovative tool aimed at bridging the gap between endocrinology, psychiatry, and neuroscience. It leverages data analytics to analyze the intricate relationships between hormonal fluctuations and mood disorders. This tool aims to provide valuable insights into how hormones impact mental health, and pave the way for novel therapeutic approaches. Furthermore, it sets the foundation for expanding our understanding of the role of hormones in neurodevelopment and signaling pathways.
- Python and R are highly recommended due to their extensive use in bioinformatics and data analysis. Python libraries such as Pandas, NumPy, SciPy, and Scikit-learn, and R packages like ggplot2, dplyr, and caret could be particularly useful.
- Bioconductor (R package), BioPython, and BioPandas are excellent tools for bioinformatics analysis.
- Matplotlib and Seaborn in Python, and ggplot2 in R can help with creating informative visuals.
- SQL or MongoDB
- Scikit-learn (Python) or Caret (R).
- Git and GitHub for version control and collaborative work.
Access to databases containing relevant hormone and mood disorder data. Examples could include PubMed, ClinicalTrials.gov, and specific hormone or neuroscience databases.
Depending on the complexity of the data analysis, high-performance computing resources might be necessary.
Collaboration with experts in endocrinology, psychiatry, and neuroscience for the interpretation of data and results.
All contributors are expected to respect each other and engage in constructive dialogue. We encourage diversity and inclusion, and we expect all interactions to be conducted professionally and respectfully. Any form of harassment, bullying, or discrimination will not be tolerated. Violations of this code of conduct may result in the offender being banned from contributing.
To contribute to this project, please first fork the repository and clone it to your local machine. Create a new branch for your changes and commit your code there. Once your changes are ready for review, submit a pull request. Please ensure that your code is clean, well-commented, and passes all tests before submitting. For major changes or additions, please open an issue for discussion first.
If you find a bug or want to suggest a new feature, please first check the issue tracker to see if it has already been reported. If not, create a new issue, providing a clear and concise description of what the issue is, steps to reproduce it (for bugs), and why you think this feature or fix would be beneficial. Please tag your issues appropriately.
We adhere to the PEP8 coding standards for Python, and the Tidyverse style guide for R. Please ensure your code is clean, well-commented, and follows these standards. Use meaningful variable names and include a brief comment for all functions explaining what they do. All code should be tested to ensure it works as expected before being submitted.