Automated Bioinformatics Analysis via AutoBA
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Abstract (arXiv)
With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow. In response to this need, we introduce Auto Bioinformatics Analysis (AutoBA), an autonomous AI agent based on a large language model designed explicitly for conventional omics data analysis. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBA's robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome sequencing (WGS), RNA sequencing (RNA-seq), single-cell RNA-seq, ChIP-seq, and spatial transcriptomics. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA deploys the analysis locally, preserving data privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents a convenient tool, offering robustness and adaptability for complex omics data analysis.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 7 of its 86 references that are in the map. First posted on arXiv 6 Sep 2023.
Builds on
7 of 86 references.
References · 7
- 2022Emergent Abilities of Large Language Modelscited by 2,512
- 2023Towards Generalist Biomedical AIcited by 289
- 2023ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Modelscited by 116
- 2021MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extractioncited by 83
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- 2023SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Modelcited by 18
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Built on by
Citing papers per year: 2024: 3; 2025: 2; 2026: 1.
6 papers.
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- 2024Bioinformatics and Biomedical Informatics with ChatGPT: Year One Reviewcited by 4
- 2026ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomicscited by 0
- 2026ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics12 Mar 2026
- 2025GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis28 Jul 2025
- 2025LIDDIA: Language-based Intelligent Drug Discovery Agent19 Feb 2025
- 2024From Intention To Implementation: Automating Biomedical Research via LLMs12 Dec 2024
- 2024Empowering Biomedical Discovery with AI Agents3 Apr 2024
- 2024Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review22 Mar 2024