# Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code

arXiv: https://arxiv.org/abs/2603.08195 · HTML: https://mlsanity.com/paper/2603.08195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 57 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
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- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [What You See is What You Read? Improving Text-Image Alignment Evaluation](https://mlsanity.com/paper/2305.10400/) (2023)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [Fast Vocabulary Transfer for Language Model Compression](https://mlsanity.com/paper/2402.09977/) (2024)
- [AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain](https://mlsanity.com/paper/2110.13434/) (2021)
- [Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study](https://mlsanity.com/paper/2401.10825/) (2024)
- [Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking](https://mlsanity.com/paper/2005.14253/) (2020)
- [On the Importance of Data Size in Probing Fine-tuned Models](https://mlsanity.com/paper/2203.09627/) (2022)
- [Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting](https://mlsanity.com/paper/2402.12801/) (2024)
- [SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles](https://mlsanity.com/paper/2108.09070/) (2021)
- [WorkflowHub: a registry for computational workflows](https://mlsanity.com/paper/2410.06941/) (2024)
- [A large dataset of software mentions in the biomedical literature](https://mlsanity.com/paper/2209.00693/) (2022)
- [Who's Who? LLM-assisted Software Traceability with Architecture Entity Recognition](https://mlsanity.com/paper/2511.02434/) (2025)
- [Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts](https://mlsanity.com/paper/2309.01812/) (2023)
- [Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows](https://mlsanity.com/paper/2411.19295/) (2024)

## Abstract (arXiv)

Motivation: The rapid growth of biological data has intensified the need for transparent, reproducible, and well-documented computational workflows. The ability to clearly connect the steps of a workflow in the code with their description in a paper would improve workflow comprehension, support reproducibility, and facilitate reuse. This task requires the linking of bioinformatics tools in workflow code with their mentions in a published workflow description. Results: We present CoPaLink, an automated approach that integrates three components: named entity recognition (NER) for identifying tool mentions in scientific text, NER for tool mentions in workflow code, and entity resolution based on word embedding similarity. We propose approaches for all three steps, achieving a high individual F1-measure (77 - 90) and a joint accuracy of 66 when evaluated on Nextflow workflows using Sentence-BERT. CoPaLink leverages corpora of scientific articles and workflow executable code with curated tool annotations to bridge the gap between narrative descriptions and workflow implementations. Availability: The code is available at https://gitlab.liris.cnrs.fr/sharefair/copalink-experiments and https://gitlab.liris.cnrs.fr/sharefair/copalink. The corpora are also available: CPL-Article (https://doi.org/10.5281/zenodo.20746904), CPL-Code (https://doi.org/10.5281/zenodo.20746970) and CPL-Gold-Entity-Resolution (https://doi.org/10.5281/zenodo.20746994).