Bayesian Posterior Integration for Classification of Mass Spectrometry Data

Webb‐Robertson, Bobbie‐Jo; Metz, Thomas O.; Waters, Katrina M.; Zhang, Qibin; Rewers, Marian

doi:10.1007/978-3-319-45809-0_11

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…However, traditional in vivo toxicity testing is limited in its throughput and capacity to provide mechanistic information linking apical end points to the underlying processes involved in chemical toxicity . Current “-omics” level approaches evaluating PAH carcinogenicity have focused on identifying chemical-specific mechanisms and been largely successful contributing toward development of predictive approaches of chemical MOA. ,− However, past studies have not focused on identifying broader patterns in affected biomolecules across different parameters of PAHs, such as cancer risk. Here, we evaluated the transcriptional signatures associated with a range of carcinogenic PAHs tested in a 3D in vitro airway epithelium and utilized a WGCNA approach to identify gene modules significantly correlated to increasing cancer risk calculated through RPF.…”

Section: Discussionmentioning

confidence: 99%

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Chang

Rager

Tilton

2021

Chem. Res. Toxicol.

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Exposure to polycyclic aromatic hydrocarbons (PAHs) often occurs as complex chemical mixtures, which are linked to numerous adverse health outcomes in humans, with cancer as the greatest concern. The cancer risk associated with PAH exposures is commonly evaluated using the relative potency factor (RPF) approach, which estimates PAH mixture carcinogenic potential based on the sum of relative potency estimates of individual PAHs, compared to benzo[a]pyrene (BAP), a reference carcinogen. The present study evaluates molecular mechanisms related to PAH cancer risk through integration of transcriptomic and bioinformatic approaches in a 3D human bronchial epithelial cell model. Genes with significant differential expression from human bronchial epithelium exposed to PAHs were analyzed using a weighted gene coexpression network analysis (WGCNA) two-tiered approach: first to identify gene sets comodulated to RPF and second to link genes to a more comprehensive list of regulatory values, including inhalation-specific risk values. Over 3000 genes associated with processes of cell cycle regulation, inflammation, DNA damage, and cell adhesion processes were found to be comodulated with increasing RPF with pathways for cell cycle S phase and cytoskeleton actin identified as the most significantly enriched biological networks correlated to RPF. In addition, comodulated genes were linked to additional cancer-relevant risk values, including inhalation unit risks, oral cancer slope factors, and cancer hazard classifications from the World Health Organization’s International Agency for Research on Cancer (IARC). These gene sets represent potential biomarkers that could be used to evaluate cancer risk associated with PAH mixtures. Among the values tested, RPF values and IARC categorizations shared the most similar responses in positively and negatively correlated gene modules. Together, we demonstrated a novel manner of integrating gene sets with chemical toxicity equivalence estimates through WGCNA to understand potential mechanisms.

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Section: Discussionmentioning

confidence: 99%

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Chang

Rager

Tilton

2021

Chem. Res. Toxicol.

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“…The advantage of this method is that class memberships are easy and fast to compute; however, it is not guaranteed to perform well when the assumptions are not met. These models have many applications in bioinformatics, such as RNA sequences classification (Knight, Ivanov, & Dougherty, 2014;Wang, Garrity, Tiedje, & Cole, 2007), prediction of PPI sites (Murakami & Mizuguchi, 2010), and mass spectrometry data analysis (Webb-Robertson, Metz, Waters, Zhang, & Rewers, 2017). It has also been adopted in MRI-based diagnostic of pathological brains (Zhou et al, 2015).…”

Section: Bayesian Classifiersmentioning

confidence: 99%

Machine learning for bioinformatics and neuroimaging

Serra

Galdi

Tagliaferri

2018

WIREs Data Min & Knowl

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Machine Learning (ML) is a well‐known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience. It involves computational methodologies to address complex real‐world problems and promises to enable computers to assist humans in the analysis of large, complex data sets. ML approaches have been widely applied to biomedical fields and a great body of research is devoted to this topic. The purpose of this article is to present the state‐of‐the art in ML applications to bioinformatics and neuroimaging and motivate research in new trend‐setting directions. We show how ML techniques such as clustering, classification, embedding techniques and network‐based approaches can be successfully employed to tackle various problems such as gene expression clustering, patient classification, brain networks analysis, and identification of biomarkers. We also present a short description of deep learning and multiview learning methodologies applied in these contexts. We discuss some representative methods to provide inspiring examples to illustrate how ML can be used to address these problems and how biomedical data can be characterized through ML. Challenges to be addressed and directions for future research are presented and an extensive bibliography is included. This article is categorized under: Application Areas > Health Care Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

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Bayesian Posterior Integration for Classification of Mass Spectrometry Data

Cited by 2 publications

References 28 publications

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Machine learning for bioinformatics and neuroimaging

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