Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery

Suppers, Anouk; Gool, Alain J. van; Wessels, Hans

doi:10.3390/proteomes6020020

Cited by 24 publications

(20 citation statements)

References 74 publications

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“…The translation of proteomic biomarkers to clinical practice has been limited and factors such as prefiltering, validation costs, small sample sizes, lack of instrumental standardization, and insufficient statistical analyses and overfitting are general barriers [49,126]. Unfortunately, these factors also characterize the proteomic research in the field of chronic pain conditions according to this systematic review.…”

Section: Validity and Translation To Clinical Practicementioning

confidence: 94%

“…Furthermore, available data suggest that chronic pain is a complex process involving interactions of an array of biochemical, transmitters, and receptors both in the central and peripheral nervous systems. It is highly unlikely that conditions such as chronic pain and cancer can be captured in their entirety by one biomarker as these conditions are heterogenous and the result of interacting complex cellular networks [48,49]. To date, no unidimensional reliable biomarker for pain has been identified.…”

Section: Single Molecules In Blood and Csf -Drawbacksmentioning

confidence: 99%

“…40 patients [75,76]. Suppers et al provide advice on how to optimize the sample size [49]. The importance of large sample sizes (several hundreds of subjects) for obtaining valid results was clearly illustrated in a proteomic study on obesity [100].…”

Section: Comparative Proteomics: Studies Differentiating Between Chromentioning

confidence: 99%

“…Hence, classic statistical methods such as t-tests, ANOVA (and their non-parametric alternatives), multiple regression, and logistic regression are not applicable for such datasets. Several studies used these classic statistical tests, but these methods do not consider the complex interplay between proteins forming biological networks and are associated with multiple testing problems [49]. Other studies applied advanced multivariate statistical methods capable of handling the characteristics of the datasets typical for proteomic studies.…”

Section: Comparative Proteomics: Studies Differentiating Between Chromentioning

confidence: 99%

“…a system biology approach). Pros and cons of suitable advanced multivariate methods have been described elsewhere [49,126,127].…”

Section: Comparative Proteomics: Studies Differentiating Between Chromentioning

confidence: 99%

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Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses

Gerdle

Ghafouri

2020

Expert Review of Proteomics

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Introduction: The lack of biomarkers indicating involved nociceptive and/or pain mechanisms makes diagnostic procedures problematic. Clinical pain research has begun to use proteomics. Areas covered: This systematic review covers proteomic studies of chronic pain cohorts and in relation to clinical variables. Searches in three databases identified 96 studies from PubMed, 161 from Scopus and 155 from Web of Science database. Finally, 27 relevant articles were included. Network analyses based on the identified proteins were performed. Expert opinion: Small pain cohorts were investigated and the number of studies per diagnosis and tissue is small. The use of proteomics in chronic pain research is exploratory and larger proteomic studies are needed. It will be necessary to standardize the descriptions of the pain cohorts investigated. There is a need to identify the mechanisms underlying the whole clinical presentation of specific chronic pain conditions. Multivariate methods capable of handling and identifying intercorrelated protein patterns must be applied. Rather than focusing on a few proteins, future studies should use network analyses to investigate interactions and biological processes. Proteomics in combination with bioinformatics have a huge potential to identify previously unknown panels of proteins involved in chronic pain and relevant when devising new pain control strategies.

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Section: Validity and Translation To Clinical Practicementioning

confidence: 94%

Section: Single Molecules In Blood and Csf -Drawbacksmentioning

confidence: 99%

Section: Comparative Proteomics: Studies Differentiating Between Chromentioning

confidence: 99%

Section: Comparative Proteomics: Studies Differentiating Between Chromentioning

confidence: 99%

“…a system biology approach). Pros and cons of suitable advanced multivariate methods have been described elsewhere [49,126,127].…”

Section: Comparative Proteomics: Studies Differentiating Between Chromentioning

confidence: 99%

See 3 more Smart Citations

Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses

Gerdle

Ghafouri

2020

Expert Review of Proteomics

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A review of explainable and interpretable AI with applications in COVID‐19 imaging

Fuhrman

Gorre

et al. 2021

Medical Physics

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The development of medical imaging AI systems for evaluating COVID‐19 patients has demonstrated potential for improving clinical decision‐making and assessing patient outcomes during the recent COVID‐19 pandemic. These have been applied to many medical imaging tasks including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life‐or‐death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to Covid‐19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID‐19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID‐19 to quickly understand the basics of several explainable AI techniques and assist in selection of an approach that is both appropriate and effective for a given scenario. This article is protected by copyright. All rights reserved

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Exploring quality evaluation markers of Fructus Psoraleae based on chemometric analysis integrated with network pharmacology

Zhao,

Guo,

et al. 2023

Phytochemical Analysis

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IntroductionFructus Psoraleae (FP) is a well‐known traditional Chinese medicine for the treatment of osteoporosis. However, major quality differences were witnessed owing to its various origins, thus influencing its safety and efficacy.ObjectivesThe study aimed to evaluate the quality of FP from different origins and predict its quality evaluation markers.MethodsUltra‐high‐performance liquid chromatography coupled with quadrupole time‐of‐flight tandem mass spectrometry was employed for tentative characterisation of the constituents in 10 batches of FP, followed by the utilisation of multivariate statistical analysis methods including principal component analysis and orthogonal partial least squares discriminant analysis for quality evaluation. Network pharmacology approaches were utilised to explore the underlying mechanism of the screened chemotaxonomic markers in treating osteoporosis.ResultsForty‐one components in FP including, chalcones, coumarins, coumestans, flavonoids, iso‐flavonoids, and phenolics, were characterised based on their fragmentation pathways. Ten batches of FP were basically divided into three categories, and eight chemotaxonomic markers including isopsoralen, calamenene, bakuchiol, psoralen, bavachinin, isoneobavaisoflavone, corylifol C, and neobavaisoflavone were screened. Network pharmacology revealed that the chemotaxonomic markers can act on targets such as AKT1, HSP90AA1, and EGFR and possess effects mainly through glycolysis and wnt/β‐catenin signalling to alleviate osteoporosis. Molecular docking and molecular dynamic simulation confirmed the good binding affinity and stability between proteins and selected markers. So, eight chemotaxonomic markers were all preferentially recommended as quality evaluation markers.ConclusionThe study not only provides a reference for the improvement of quality control of FP but also offers a theoretical basis for its further in‐depth research in osteoporosis.

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Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery

Cited by 24 publications

References 74 publications

Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses

Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses

A review of explainable and interpretable AI with applications in COVID‐19 imaging

Exploring quality evaluation markers of Fructus Psoraleae based on chemometric analysis integrated with network pharmacology

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