PromptBio: An Agentic Platform for End-to-End Computational Biomedical Research

In the rapidly evolving realm of biomedical research, the intricacy of computational analyses often dwarfs the ability to translate a scientific inquiry into a dependable workflow. This gap in capability has long been a hurdle for researchers, demanding extensive technical mastery and arduous manual coordination. To bridge this chasm, PromptBio has emerged as an innovative multi-agent AI platform accessible via a web portal at https://promptbio.ai.

At the helm of PromptBio's agent system is PromptGenie, a sophisticated tool that interacts with researchers using natural language. PromptGenie takes a research objective and converts it into a meticulously documented plan, executes the necessary research and analysis, and dynamically adjusts subsequent steps as new evidence and findings unfold. This platform masterfully blends reasoning with controlled execution, while ensuring human oversight and an unassailable record of the research process.

PromptBio's prowess lies in its adaptability; it possesses the capability to utilize established methods, devise bespoke analyses, and integrate external workflows. This expansive adaptability empowers researchers to transcend rigid pipelines, while simultaneously championing the principles of reproducibility. To substantiate these claims, the platform was rigorously evaluated through benchmarks of end-to-end bioinformatics analysis and biomedical deep research.

It also underwent validation of representative omics and machine-learning capabilities, and was subjected to a hypothesis-driven regulatory-genomics case study.

The results of these evaluations were overwhelmingly positive. PromptGenie demonstrated superior analytical accuracy and superior evidence retrieval and synthesis capabilities compared to its counterparts. The platform's domain skills produced results that were consistent with established methods, thereby affirming its practical utility.

A case study further showcased PromptBio's potential by integrating human-specific genomic and epigenomic features to explore cortical development. These findings collectively underscore the platform's ability to orchestrate intricate biomedical analyses under the guidance of researchers, thereby providing an accessible means for accelerating scientific iteration and metamorphosing research questions into transparent, reusable computational workflows.