Predictive Modeling for Metabolomics Data

Ghosh, Tusharkanti; Zhang, Weimin; Ghosh, Debashis; Kechris, Katerina

doi:10.1007/978-1-0716-0239-3_16

Cited by 50 publications

(33 citation statements)

References 79 publications

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“…A random forest (RF) algorithm is an extremely reliable classifier and has become popular as a biomarker detection tool in various metabolomics studies. Using RF as a classifier has the following advantages: simple theory, fast speed, stable and insensitive to noise, little or no overfitting, and automatic compensation mechanism on biased sample numbers of groups 44 – 46 . It constructs an ensemble of decision trees, which is a combination of tree-structured predictors.…”

Section: Discussionmentioning

confidence: 99%

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease

Ghosh

Choudhury

Bhattacharyya³

et al. 2021

Sci Rep

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Asthma—chronic obstructive pulmonary disease (COPD) overlap, termed as ACO, is a complex heterogeneous disease characterised by persistent airflow limitation, which manifests features of both asthma and COPD. These patients have a worse prognosis, in terms of more frequent and severe exacerbations, more frequent symptoms, worse quality of life, increased comorbidities and a faster lung function decline. In absence of clear diagnostic or therapeutic guidelines, ACO presents as a challenge to clinicians. The present study aims to investigate whether ACO patients have a distinct exhaled breath condensate (EBC) metabolic profile in comparison to asthma and COPD. A total of 132 age and BMI matched male smokers were recruited in the exploratory phase which consisted of (i) controls = 33 (ii) asthma = 34 (iii) COPD = 30 and (iv) ACO = 35. Using nuclear magnetic resonance (NMR) metabolomics, 8 metabolites (fatty acid, propionate, isopropanol, lactate, acetone, valine, methanol and formate) were identified to be significantly dysregulated in ACO subjects when compared to both, asthma and COPD. The expression of these dysregulated metabolites were further validated in a fresh patient cohort consisting of (i) asthma = 32 (ii) COPD = 32 and (iii) ACO = 40, which exhibited a similar expression pattern. Multivariate receiver operating characteristic (ROC) curves generated using these metabolites provided a robust ACO classification model. The findings were also integrated with previously identified serum metabolites and inflammatory markers to develop a robust predictive model for differentiation of ACO. Our findings suggest that NMR metabolomics of EBC holds potential as a platform to identify robust, non-invasive biomarkers for differentiating ACO from asthma and COPD.

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Section: Discussionmentioning

confidence: 99%

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease

Ghosh

Choudhury

Bhattacharyya³

et al. 2021

Sci Rep

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“…A significant drawback of SVM is its restrictions on binary classification problems. For example, it can only discriminate between two classes where the data points are categorised by two classes in n-dimensional space, where n corresponds to the number of metabolites [100]. RF belongs to the family of classification trees and is found to be the best classifier [101,102].…”

Section: Biological Interpretationmentioning

confidence: 99%

Current State of Metabolomics Research in Meat Quality Analysis and Authentication

Tao

Chen

Xie

et al. 2021

Foods

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In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

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“…These types of methods are also used to predict clinical outcomes, like the effectiveness of a drug, or for the identification of gene-gene and gene-environment interactions. See Libbrecht and Noble [48], Ghosh et al [49], and Zhou and Gaillins [50] for a review of ML methods applied in specific types of 'omics data, or for integrating multiple 'omics data sources [51,52].…”

Section: How ML Is Used In Clinical and Translational Researchmentioning

confidence: 99%

Lessons and tips for designing a machine learning study using EHR data

Arbet

Brokamp

Meinzen‐Derr

et al. 2020

J. Clin. Trans. Sci.

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Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or pre-specified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record (EHR). The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.

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Predictive Modeling for Metabolomics Data

Cited by 50 publications

References 79 publications

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease

Current State of Metabolomics Research in Meat Quality Analysis and Authentication

Lessons and tips for designing a machine learning study using EHR data

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