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Yang, Qingxia; Wang, Yun‐Xia; Li, Feng‐Cheng; Zhang, Song; Luo, Yongchao; Li, Yang; Tang, Jing; Li, Bo; Chen, Yu‐Zong; Xue, Weiwei; Zhu, Feng

doi:10.1111/cns.13289

Cited by 3 publications

(4 citation statements)

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“…26,27 Thus, the reproducibility of biomarkers discovered in different subsets is regarded as an essential criterion for assessing the performance of biomarker discovery methods. 28,29 For this criterion, a multiclass dataset was divided into three different sub-datasets by random sampling. Here, stratified random sampling was applied, which involved the division of all samples into different strata (multiple classes), and samples were selected randomly from each stratum.…”

Section: Collection Of Multiple Criteria For Comprehensive Assessment...mentioning

confidence: 99%

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

2023

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Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

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Section: Collection Of Multiple Criteria For Comprehensive Assessment...mentioning

confidence: 99%

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

2023

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“…The cinchonidine has been widely used for its antitumor properties and has served as a treatment for malaria for the past few decades. 11,12 As an important pharmaceutical intermediate in organic synthesis, cinchonidine plays an important role in the research and development process of chemical and pharmaceutical medicine. The median lethal dose of cinchonidine is 206 mg kg −1 .…”

Section: Introductionmentioning

confidence: 99%

“…Chiral substances are highly valued for their unique chemical properties, which have a signicant impact on living organisms. [3][4][5][6][7][8][9][10][11][12][13][14][15] These unique characteristics enable them to be widely applied in pharmaceutical engineering, bioimaging technology, food industry, and other elds. [16][17][18][19][20] For example, BINOL, a compound with a chiral symmetrical center and a rigid conjugate structure, and its derivatives are notable chiral agents that are commonly used in asymmetric synthesis and catalysis.…”

Section: Introductionmentioning

confidence: 99%

Self-assembled bamboo-like carbon nanotubes based on chiral H₈BINOL sensors to recognize cinchonidine efficiently by diastereoisomer complexes

Li,

Sun,

Sun

et al. 2024

RSC Adv.

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“…8,9 Due to the differences in the principles of these classification methods, conflicting outcomes are observed when different classification methods are applied, even for the same data set. 10,11 Therefore, it is highly necessary to apply an appropriate classification method with superior performance for a specific data set. To select the most suitable method, the performance of all classification methods must be assessed using well-established criteria.…”

Section: ■ Introductionmentioning

confidence: 99%

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Yang,

Chen,

Jiang

et al. 2024

Anal. Chem.

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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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Abstract: Front cover: The cover image is based on the original article Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence‐based method of enhanced reproducibility by Qing‐Xia Yan et al., https://doi.org/10.1111/cns.13196.

Cited by 3 publications

References 0 publications

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Self-assembled bamboo-like carbon nanotubes based on chiral H₈BINOL sensors to recognize cinchonidine efficiently by diastereoisomer complexes

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

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Abstract: Front cover: The cover image is based on the original article Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence‐based method of enhanced reproducibility by Qing‐Xia Yan et al., https://doi.org/10.1111/cns.13196.

Cited by 3 publications

References 0 publications

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Self-assembled bamboo-like carbon nanotubes based on chiral H8BINOL sensors to recognize cinchonidine efficiently by diastereoisomer complexes

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

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Product

Resources

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Self-assembled bamboo-like carbon nanotubes based on chiral H₈BINOL sensors to recognize cinchonidine efficiently by diastereoisomer complexes