Applications of network-based survival analysis methods for pathways detection in cancer

Antonella Iuliano; Annalisa Occhipinti; Claudia Angelini; Italia De Feis; Pietro Lió

doi:10.1007/978-3-319-24462-4_7

Applications of network-based survival analysis methods for pathways detection in cancer

Antonella Iuliano, Annalisa Occhipinti, Claudia Angelini, Italia De Feis, Pietro Lió

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for highdimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression. Using these network-based models, we show how to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to prognosis and survival in different types of tumors. Comparisons are carried out on three real different cancer datasets.

Original language	English
Title of host publication	Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers
Editors	Pietro Lio, Clelia di Serio, Alessandro Nonis, Roberto Tagliaferri
Place of Publication	Cambridge
Publisher	Springer Verlag
Pages	76-88
Number of pages	13
ISBN (Print)	9783319244617
DOIs	https://doi.org/10.1007/978-3-319-24462-4_7
Publication status	Published - 18 Nov 2015
Event	11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014 - Cambridge, United Kingdom Duration: 26 Jun 2014 → 28 Jun 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8623
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014
Country/Territory	United Kingdom
City	Cambridge
Period	26/06/14 → 28/06/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Access to Document

10.1007/978-3-319-24462-4_7Licence: Unspecified

Cite this

Iuliano, A., Occhipinti, A., Angelini, C., De Feis, I., & Lió, P. (2015). Applications of network-based survival analysis methods for pathways detection in cancer. In P. Lio, C. di Serio, A. Nonis, & R. Tagliaferri (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers (pp. 76-88). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8623). Springer Verlag. https://doi.org/10.1007/978-3-319-24462-4_7

Iuliano, Antonella ; Occhipinti, Annalisa ; Angelini, Claudia et al. / Applications of network-based survival analysis methods for pathways detection in cancer. Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers. editor / Pietro Lio ; Clelia di Serio ; Alessandro Nonis ; Roberto Tagliaferri. Cambridge : Springer Verlag, 2015. pp. 76-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for highdimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression. Using these network-based models, we show how to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to prognosis and survival in different types of tumors. Comparisons are carried out on three real different cancer datasets.",

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Iuliano, A, Occhipinti, A, Angelini, C, De Feis, I & Lió, P 2015, Applications of network-based survival analysis methods for pathways detection in cancer. in P Lio, C di Serio, A Nonis & R Tagliaferri (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8623, Springer Verlag, Cambridge , pp. 76-88, 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014, Cambridge, United Kingdom, 26/06/14. https://doi.org/10.1007/978-3-319-24462-4_7

Applications of network-based survival analysis methods for pathways detection in cancer. / Iuliano, Antonella; Occhipinti, Annalisa; Angelini, Claudia et al.
Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers. ed. / Pietro Lio; Clelia di Serio; Alessandro Nonis; Roberto Tagliaferri. Cambridge : Springer Verlag, 2015. p. 76-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8623).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Applications of network-based survival analysis methods for pathways detection in cancer

AU - Iuliano, Antonella

AU - Occhipinti, Annalisa

AU - Angelini, Claudia

AU - De Feis, Italia

AU - Lió, Pietro

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015/11/18

Y1 - 2015/11/18

N2 - Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for highdimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression. Using these network-based models, we show how to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to prognosis and survival in different types of tumors. Comparisons are carried out on three real different cancer datasets.

AB - Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for highdimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression. Using these network-based models, we show how to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to prognosis and survival in different types of tumors. Comparisons are carried out on three real different cancer datasets.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers

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A2 - Nonis, Alessandro

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ER -

Iuliano A, Occhipinti A, Angelini C, De Feis I, Lió P. Applications of network-based survival analysis methods for pathways detection in cancer. In Lio P, di Serio C, Nonis A, Tagliaferri R, editors, Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, CIBB 2014, Revised Selected Papers. Cambridge : Springer Verlag. 2015. p. 76-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24462-4_7

Applications of network-based survival analysis methods for pathways detection in cancer

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