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Oral Biology pp 347-364 | Cite as

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques

  • Moritz KebschullEmail author
  • Panos N. Papapanou
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1537)

Abstract

Although contemporary high-throughput –omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.

Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.

Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of –omics data.

Key words

Periodontal disease Aggressive periodontitis Chronic periodontitis Gene expression Transcriptome Gingiva Classification Machine learning 

Notes

Acknowledgments

This work was supported by grants from the German Society for Periodontology (DG PARO) and the German Society for Oral and Maxillo-Facial Sciences (DGZMK) to M.K., and by grants from NIH/NIDCR (DE015649 and DE024735) and by an unrestricted gift from Colgate-Palmolive Inc. to P.N.P. The authors thank Prof. Anne-Laure Boulesteix (Munich, Germany) and Prof. Bettina Grün (Linz, Austria) for their support with the CMA and flexmix packages, respectively.

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Periodontology, Operative and Preventive Dentistry, Faculty of MedicineUniversity of BonnBonnGermany
  2. 2.Division of Periodontics, Section of Oral, Diagnostic and Rehabilitation SciencesColumbia University College of Dental MedicineNew YorkUSA

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