Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States: A Systems Approach to Diagnostic Biomarkers

Matthias Dehmer; Laurin A. J. Mueller; Frank Emmert-Streib

doi:10.1371/journal.pone.0077602

Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States: A Systems Approach to Diagnostic Biomarkers

Matthias Dehmer^*
, Laurin A. J. Mueller
, Frank Emmert-Streib

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli › Tieteellinen › vertaisarvioitu

23 Sitaatiot (Scopus)

Abstrakti

Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.

Alkuperäiskieli	Englanti
Artikkeli	77602
Sivumäärä	8
Julkaisu	PLoS ONE
Vuosikerta	8
Numero	11
DOI - pysyväislinkit	https://doi.org/10.1371/journal.pone.0077602
Tila	Julkaistu - 13 marrask. 2013
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P22029-N13). The authors also thank the 'Zentraler Informatikdienst' of the Technical University of Vienna for providing computing resources to perform large scale computations on the Phoenix Cluster. Also, Matthias Dehmer and Laurin Mueller thank the Standortagentur Tirol for supporting this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

SDG 3 – Hyvä terveys ja hyvinvointi

Pääsy asiakirjaan

10.1371/journal.pone.0077602

Siteeraa tätä

@article{7c70324a99b14bebb8d30148c13ab96d,

title = "Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States: A Systems Approach to Diagnostic Biomarkers",

abstract = "Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.",

keywords = "GENE-EXPRESSION, TOPOLOGICAL INDEXES, INFORMATION CONTENT, ENTROPY MEASURES, GRAPHS, COMPLEXITY, DISCRIMINATION, CELLS",

author = "Matthias Dehmer and Mueller, \{Laurin A. J.\} and Frank Emmert-Streib",

year = "2013",

month = nov,

day = "13",

doi = "10.1371/journal.pone.0077602",

language = "English",

volume = "8",

journal = "PLoS ONE",

issn = "1932-6203",

publisher = "Public Library of Science",

number = "11",

}

TY - JOUR

T1 - Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States

T2 - A Systems Approach to Diagnostic Biomarkers

AU - Dehmer, Matthias

AU - Mueller, Laurin A. J.

AU - Emmert-Streib, Frank

PY - 2013/11/13

Y1 - 2013/11/13

N2 - Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.

AB - Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.

KW - GENE-EXPRESSION

KW - TOPOLOGICAL INDEXES

KW - INFORMATION CONTENT

KW - ENTROPY MEASURES

KW - GRAPHS

KW - COMPLEXITY

KW - DISCRIMINATION

KW - CELLS

U2 - 10.1371/journal.pone.0077602

DO - 10.1371/journal.pone.0077602

M3 - Article

SN - 1932-6203

VL - 8

JO - PLoS ONE

JF - PLoS ONE

IS - 11

M1 - 77602

ER -

Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States: A Systems Approach to Diagnostic Biomarkers

Abstrakti

Rahoitus

YK:n kestävän kehityksen tavoitteet

Pääsy asiakirjaan

Sormenjälki

Siteeraa tätä