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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Hrsg. v. Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Wissenschaftlicher Beirat: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan


CiteScore 2018: 0.90

SCImago Journal Rank (SJR) 2018: 0.315

Open Access
Online
ISSN
1613-4516
Alle Formate und Preise
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Band 4, Heft 3

Hefte

Supervised classification of combined copy number and gene expression data

S. Riccadonna
  • FBK-irst, via Sommarive 18, I-38100, Povo (Trento), http://mpa.itc.it, Italy
  • DIT, University of Trento, via Sommarive 14, I-38100, Povo (Trento), http://dit.unitn.it, Italy
  • Weitere Artikel des Autors:
  • De Gruyter OnlineGoogle Scholar
/ G. Jurman / S. Merler / S. Paoli
  • FBK-irst, via Sommarive 18, I-38100 Povo (Trento), http://mpa.itc.it Italy
  • DIT, University of Trento, via Sommarive 14, I-38100 Povo (Trento), http://dit.unitn.it, Italy
  • Weitere Artikel des Autors:
  • De Gruyter OnlineGoogle Scholar
/ A. Quattrone
  • CIBIO and DISI, University of Trento, via Sommarive 14, I-38100 Povo (Trento), http://www.unitn.it, Italy
  • Weitere Artikel des Autors:
  • De Gruyter OnlineGoogle Scholar
/ C. Furlanello
Online erschienen: 18.10.2016 | DOI: https://doi.org/10.1515/jib-2007-74

Summary

In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

Artikelinformationen

Online erschienen: 18.10.2016

Erschienen im Druck: 01.12.2007


Quellenangabe: Journal of Integrative Bioinformatics, Band 4, Heft 3, Seiten 168–185, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2007-74.

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© 2007 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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[1]
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IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014, Jahrgang 11, Nummer 1, Seite 168
[2]
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PLoS ONE, 2012, Jahrgang 7, Nummer 3, Seite e32200

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