代谢组学
预处理器
规范化(社会学)
数据预处理
降维
计算机科学
数据库规范化
鉴定(生物学)
数据挖掘
化学
人工智能
模式识别(心理学)
色谱法
生物
社会学
植物
人类学
作者
Yu-Jen Liang,Chih-Ting Yang,Chia‐Wei Chen,Yin-Chun Lin,Shu‐Yao Lin,Yi‐Sheng Wang,Hsin‐Chou Yang
标识
DOI:10.1021/acs.analchem.5c03225
摘要
Metabolomics has experienced significant growth and increased popularity due to technological advancements. We introduced an integrated tool for untargeted metabolomics analysis, SMART 1.0, that streamlined the entire analysis process, from initial data preprocessing to subsequent association analysis. With SMART 2.0, we enhanced SMART 1.0 by introducing new analytical modules in targeted metabolomics analysis, data normalization, quality control assessment, and advanced dimensionality reduction and classification methods. Additionally, SMART 2.0 offers integrative omics pathway analysis and postanalysis tasks such as peak identification and concentration calibration. We also explored the potential of using large language models for peak annotation and have found the results to be promising. This study employs narcotics data and breast cancer data as demonstrative examples to illustrate the new functionalities. The codes, a user guide, and example data can be downloaded at https://github.com/YuJenL/SMART.
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