An AI Agent for Fully Automated Multi‐Omic Analyses

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle bioinformatics analysis continues to grow. In response to this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models (LLMs). AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. In comparison to ChatGPT and open-source LLMs, an automated code repair (ACR) mechanism in AutoBA is designed to improve its stability in automated end-to-end bioinformatics analysis tasks. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2024-10-03
DOI
https://doi.org/10.1002/advs.202407094
Citations
49
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
13.97

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An AI Agent for Fully Automated Multi‐Omic Analyses

Xiaopeng Xu, Chencheng Xu, Xiuying Chen, Wenjia He et al.
49 citations
Advanced Science
Cancer Genomics and Diagnostics
13.97
article

An AI Agent for Fully Automated Multi‐Omic Analyses

Xiaopeng Xu, Chencheng Xu, Xiuying Chen, Wenjia He, Juexiao Zhou, Siyuan Chen, Haoyang Li, Bin Zhang, Guowei Li, Liwei Liu, Xin Gao
article en
49 citations

Abstract

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle bioinformatics analysis continues to grow. In response to this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models (LLMs). AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. In comparison to ChatGPT and open-source LLMs, an automated code repair (ACR) mechanism in AutoBA is designed to improve its stability in automated end-to-end bioinformatics analysis tasks. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.

Advanced ScienceVol. 11(44)
Huawei Technologies (China) (CN), King Abdullah University of Science and Technology (SA)
King Abdullah University of Science and Technology, Global Collaborative Research, King Abdullah University of Science and Technology
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Cancer Genomics and Diagnostics
13.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.