代谢组学
构造(python库)
多类分类
鉴定(生物学)
人工智能
班级(哲学)
计算机科学
机器学习
计算生物学
支持向量机
生物信息学
生物
植物
程序设计语言
作者
Qingxia Yang,Jinjun Chen,Wenyu Jiang,Lan Mi,Jiarui Liu,Hao Yu,Xinglai Ji,Jun Wang,Feng Zhu
标识
DOI:10.1021/acs.analchem.3c03212
摘要
Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.
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