Exploring genome-wide expression profiles using machine learning techniques

Moritz Kebschull, Panos N. Papapanou

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages347-364
Number of pages18
DOIs
Publication statusPublished - 2017

Publication series

NameMethods in Molecular Biology
Volume1537
ISSN (Print)1064-3745

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media LLC 2017.

Funding

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.

FundersFunder number
Colgate-Palmolive Inc.
German Society for Oral and Maxillo-Facial Sciences
German Society for Periodontology
National Institutes of Health
National Institute of Dental and Craniofacial ResearchDE024735, DE015649, R21DE021820
Deutsche Gesellschaft für Zahn-, Mund- und Kieferheilkunde
Chinese Medical Association

    ASJC Scopus Subject Areas

    • Molecular Biology
    • Genetics

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