Classification of cancer patients using pathway analysis and network clustering

David C Y Fung; Amy Lo; Lucy Jankova; Stephan J. Clarke; Mark Molloy; Graham R. Robertson; Marc R. Wilkins

doi:10.1007/978-1-61779-276-2_15

Classification of cancer patients using pathway analysis and network clustering

David C Y Fung, Amy Lo, Lucy Jankova, Stephan J. Clarke, Mark Molloy, Graham R. Robertson, Marc R. Wilkins^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Citations (Scopus)

Abstract

Molecular expression patterns have often been used for patient classification in oncology in an effort to improve prognostic prediction and treatment compatibility. This effort is, however, hampered by the highly heterogeneous data often seen in the molecular analysis of cancer. The lack of overall similarity between expression profiles makes it difficult to partition data using conventional data mining tools. In this chapter, the authors introduce a bioinformatics protocol that uses REACTOME pathways and patient-protein network structure (also called topology) as the basis for patient classification.

Original language	English
Title of host publication	Network Biology: Methods and Applications
Editors	Gerard Cagney, Andrew Emili
Place of Publication	New York
Publisher	Humana Press
Pages	311-336
Number of pages	26
Volume	781
ISBN (Print)	9781617792755
DOIs	https://doi.org/10.1007/978-1-61779-276-2_15
Publication status	Published - 2011

Publication series

Name	Methods in Molecular Biology
Volume	781
ISSN (Print)	10643745

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10.1007/978-1-61779-276-2_15

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Fung, D. C. Y., Lo, A., Jankova, L., Clarke, S. J., Molloy, M., Robertson, G. R., & Wilkins, M. R. (2011). Classification of cancer patients using pathway analysis and network clustering. In G. Cagney, & A. Emili (Eds.), Network Biology: Methods and Applications (Vol. 781, pp. 311-336). (Methods in Molecular Biology; Vol. 781). Humana Press. https://doi.org/10.1007/978-1-61779-276-2_15

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title = "Classification of cancer patients using pathway analysis and network clustering",

abstract = "Molecular expression patterns have often been used for patient classification in oncology in an effort to improve prognostic prediction and treatment compatibility. This effort is, however, hampered by the highly heterogeneous data often seen in the molecular analysis of cancer. The lack of overall similarity between expression profiles makes it difficult to partition data using conventional data mining tools. In this chapter, the authors introduce a bioinformatics protocol that uses REACTOME pathways and patient-protein network structure (also called topology) as the basis for patient classification. ",

author = "Fung, {David C Y} and Amy Lo and Lucy Jankova and Clarke, {Stephan J.} and Mark Molloy and Robertson, {Graham R.} and Wilkins, {Marc R.}",

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Fung, DCY, Lo, A, Jankova, L, Clarke, SJ, Molloy, M, Robertson, GR & Wilkins, MR 2011, Classification of cancer patients using pathway analysis and network clustering. in G Cagney & A Emili (eds), Network Biology: Methods and Applications. vol. 781, Methods in Molecular Biology, vol. 781, Humana Press, New York, pp. 311-336. https://doi.org/10.1007/978-1-61779-276-2_15

Classification of cancer patients using pathway analysis and network clustering. / Fung, David C Y; Lo, Amy; Jankova, Lucy et al.
Network Biology: Methods and Applications. ed. / Gerard Cagney; Andrew Emili. Vol. 781 New York: Humana Press, 2011. p. 311-336 (Methods in Molecular Biology; Vol. 781).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Wilkins, Marc R.

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Classification of cancer patients using pathway analysis and network clustering

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