#The rtemis ecosystem

rtemis makes advanced machine learning accessible to all - scientists, clinicians, statisticians, computer scientists. It provides:

• powerful, low-code APIs: high-level functions with complete controls of the entire data pipeline.
• no-code web applications: interactive, user-friendly interfaces.

It is actively used for:

• Machine learning algorithm development.
• Applied data science, with emphasis on biomedical basic research, clinical predictive modeling, and public health.
• Health Data Science education.

rtemis consists of 10 interconnected packages that work together to provide a complete ML ecosystem.

#APIs

• rtemis: ML & visualization API in R.
• rtemis-py: ML & visualization API in Python. (currently private)
• rtemis.jl: ML & visualization API in Julia. (currently private)
• rtemisbio: Bioinformatics extension in R.
• kaimana-r: API access to state-of-the-art open source LLMs in R. (currently private)
• kaimana-py: API access to state-of-the-art open source LLMs in Python. (currently private)

#Web applications

• rtemislive: Web application for ML & visualization. (currently private; deployed at UCSF)
• rtemisSeq: Interactive protein sequence visualization.
• rtemisXt: Interactive time-series data visualization.
• kaimana: AI Agent Chatbot using state-of-the-art open source LLMs. (currently private)

#Online books

• Programming for Data Science in R: Beginner- to intermediate-level resource for R programming. Used in UCSF Biostat 213 & 214, among others.
• Programming for Data Science in Python: Python version (work in progress).
• Programming for Data Science in Julia: Julia version (work in progress).

#GenLib

GenLib is a project using kaimana to create AI-generated educational material.

#Screenshots

Some screenshots from rtemislive, the Web UI for rtemis: