Simplifying bioinformatics data analysis through conversation
Bibliographic record
Abstract
Abstract The rapid advancements in high-throughput sequencing technologies have produced a wealth of omics data, facilitating significant biological insights but presenting immense computational challenges. Traditional bioinformatics tools require substantial programming expertise, limiting accessibility for experimental researchers. Despite efforts to develop user-friendly platforms, the complexity of these tools continues to hinder efficient biological data analysis. In this paper, we introduce BioMANIA– an AI-driven, natural language-oriented bioinformatics pipeline that addresses these challenges by enabling the automatic and codeless execution of biological analyses. BioMANIA leverages large language models (LLMs) to interpret user instructions and execute sophisticated bioinformatics work-flows, integrating API knowledge from existing Python tools. By streamlining the analysis process, BioMANIA simplifies complex omics data exploration and accelerates bioinformatics research. Compared to relying on general-purpose LLMs to conduct analysis from scratch, BioMANIA, informed by domain-specific biological tools, helps mitigate hallucinations and significantly reduces the likelihood of confusion and errors. Through comprehensive benchmarking and application to diverse biological data, ranging from single-cell omics to electronic health records, we demonstrate BioMANIA’s ability to lower technical barriers, enabling more accurate and comprehensive biological discoveries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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 source (direct Gemma or distilled Codex), 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".