2023/09/06 by Juexiao Zhou, Bin Zhang, Zhou, Juexiao +11 · 1 citation
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #Artificial Intelligence (cs.AI) #Cancer Genomics and Diagnostics #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2309.03242
openalex publication_date 2023/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.