# Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications

arXiv: https://arxiv.org/abs/2409.15076 · HTML: https://mlsanity.com/paper/2409.15076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 11 of 11)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models](https://mlsanity.com/paper/2408.02442/) (2024)
- [How Well Can a Long Sequence Model Model Long Sequences? Comparing Architechtural Inductive Biases on Long-Context Abilities](https://mlsanity.com/paper/2407.08112/) (2024)
- [Misinforming LLMs: vulnerabilities, challenges and opportunities](https://mlsanity.com/paper/2408.01168/) (2024)
- [A data-supported history of bioinformatics tools](https://mlsanity.com/paper/1807.06808/) (2018)

## Abstract (arXiv)

The exponential growth in computational power and accessibility has transformed the complexity and scale of bioinformatics research, necessitating standardized documentation for transparency, reproducibility, and regulatory compliance. The IEEE BioCompute Object (BCO) standard addresses this need but faces adoption challenges due to the overhead of creating compliant documentation, especially for legacy research. This paper presents a novel approach to automate the creation of BCOs from scientific papers using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). We describe the development of the BCO assistant tool that leverages RAG to extract relevant information from source papers and associated code repositories, addressing key challenges such as LLM hallucination and long-context understanding. The implementation incorporates optimized retrieval processes, including a two-pass retrieval with re-ranking, and employs carefully engineered prompts for each BCO domain. We discuss the tool's architecture, extensibility, and evaluation methods, including automated and manual assessment approaches. The BCO assistant demonstrates the potential to significantly reduce the time and effort required for retroactive documentation of bioinformatics research while maintaining compliance with the standard. This approach opens avenues for AI-assisted scientific documentation and knowledge extraction from publications thereby enhancing scientific reproducibility. The BCO assistant tool and documentation is available at https://biocompute-objects.github.io/bco-rag/.