PromptBio: A Multi-Agent AI Platform for Bioinformatics Data Analysis
Bibliographic record
Abstract
Abstract PromptBio is a modular AI platform for scalable, reproducible, and user-adaptable bioinformatics analysis, powered by generative AI and natural language interaction. It supports three complementary modes of analysis designed to meet diverse research needs. PromptGenie is a multi-agent system that enables stepwise, human-in-the-loop workflows using prevalidated domain-standard tools. Within PromptGenie, specialized agents—including DataAgent, OmicsAgent, AnalysisAgent, and QAgent—collaborate to manage tasks such as data ingestion, pipeline execution, statistical analysis, and interactive summarization. DiscoverFlow provides integrated, automated workflows for large-scale multi-omics analysis, offering end-to-end execution and streamlined orchestration. ToolsGenie complements these modes by dynamically generating executable bioinformatics code for custom, user-defined analyses, enabling flexibility beyond standardized workflows. PromptGenie and DiscoverFlow leverage a suite of domain-specific tools, including Omics Tools for standardized omics pipelines, Analysis Tools for downstream statistical interpretation, and MLGenie for machine learning and multi-omics modeling. We present the design, capabilities, and validation of these components, highlight their integration into automated and customizable workflows, and discuss extensibility, monitoring, and compliance. PromptBio aims to democratize high-throughput bioinformatics through a large language model–powered, natural language understanding, workflow generation and agent orchestration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".