The rtemis ecosystem

AI/ML for Health+

rtemis cover

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)

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)

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

Some screenshots from rtemislive, the Web UI for rtemis:

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