EgoNet identifies differential ego-modules and pathways related to prednisolone resistance in childhood acute lymphoblastic leukemia

Jiang, Jian; Yin, Xiangyun; Song, Xuewen; Xie, Dong; Xu, Hairong; Yang, Jing; Sun, Lirong

doi:10.1080/10245332.2017.1385211

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…50 If the AUC value is close to 1, the performance of the classification method using the identified metabolic markers is excellent for the studied multiclass metabolomic data. 51 When the AUC values are within the ranges of >0.9, ≤ 0.9, > 0.7, and ≤0.7, the corresponding methods are categorized into those with superior, good, and poor performances, respectively.…”

Section: Comprehensive Collection Of Methods For Metabolicmentioning

confidence: 99%

“…Based on the identified metabolic markers, a multiclass classification model can be constructed using a specific classification method. , Both the receiver operating characteristic ( ROC ) curve and the area under the curve ( AUC ) value were applied to quantitatively assess the performance of the classification model . If the AUC value is close to 1, the performance of the classification method using the identified metabolic markers is excellent for the studied multiclass metabolomic data . When the AUC values are within the ranges of >0.9, ≤ 0.9, > 0.7, and ≤0.7, the corresponding methods are categorized into those with superior, good, and poor performances, respectively.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Comprehensive Collection Of Methods For Metabolicmentioning

confidence: 99%

Section: Methodsmentioning

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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“…A large AUC value indicates a classifier with high classification performance. The AUC value is close to 1, which refers to a perfect classification performance . For assessing different methods, 107 overlapping markers for the three subgroups identified by RF-RFE were applied to construct classification models using the above nine methods.…”

Section: Performance Evaluation Of Machine Learning Methodsmentioning

confidence: 99%

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Gong,

Ding,

Wang

et al. 2023

J. Chem. Inf. Model.

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A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Yang

Hong

et al. 2019

Briefings in Bioinformatics

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Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the ‘thorough’ removal of unwanted variations, the collective consideration of multiple criteria (‘intragroup variation’, ‘marker stability’ and ‘classification capability’) was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were ‘first’ discovered to be non-CWP. ‘Then’, 21 new strategies that combined the ‘sample’-based method with the ‘metabolite’-based one were found to be CWP. ‘Finally’, a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

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EgoNet identifies differential ego-modules and pathways related to prednisolone resistance in childhood acute lymphoblastic leukemia

Abstract: One differential ego-network module identified in childhood ALL resistance to prednisolone based on DCN and EgoNet, might be helpful to reveal the mechanisms underlying prednisolone resistance in childhood ALL.

Cited by 5 publications

References 36 publications

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

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

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

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