An algorithm for finding gene signatures supervised by survival time data

Stefano M. Pagnotta, Michele Ceccarelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Signature learning from gene expression consists into selecting a subset of molecular markers which best correlate with prognosis. It can be cast as a feature selection problem. Here we use as optimality criterion the separation between survival curves of clusters induced by the selected features. We address some important problems in this fields such as developing an unbiased search procedure and significance analysis of a set of generated signatures. We apply the proposed procedure to the selection of gene signatures for Non Small Lung Cancer prognosis by using a real data-set.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages568-578
Number of pages11
Volume6881 LNAI
EditionPART 1
DOIs
Publication statusPublished - 29 Sep 2011
Externally publishedYes
Event15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011 - Kaiserslautern, Germany
Duration: 12 Sep 201114 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6881 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011
CountryGermany
CityKaiserslautern
Period12/9/1114/9/11

Fingerprint

Survival Time
Gene expression
Feature extraction
Signature
Genes
Prognosis
Gene
Lung Cancer
Optimality Criteria
Correlate
Feature Selection
Gene Expression
Curve
Subset

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Pagnotta, S. M., & Ceccarelli, M. (2011). An algorithm for finding gene signatures supervised by survival time data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6881 LNAI, pp. 568-578). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6881 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-23851-2_58

An algorithm for finding gene signatures supervised by survival time data. / Pagnotta, Stefano M.; Ceccarelli, Michele.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6881 LNAI PART 1. ed. 2011. p. 568-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6881 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pagnotta, SM & Ceccarelli, M 2011, An algorithm for finding gene signatures supervised by survival time data. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 6881 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6881 LNAI, pp. 568-578, 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011, Kaiserslautern, Germany, 12/9/11. https://doi.org/10.1007/978-3-642-23851-2_58
Pagnotta SM, Ceccarelli M. An algorithm for finding gene signatures supervised by survival time data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 6881 LNAI. 2011. p. 568-578. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-23851-2_58
Pagnotta, Stefano M. ; Ceccarelli, Michele. / An algorithm for finding gene signatures supervised by survival time data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6881 LNAI PART 1. ed. 2011. pp. 568-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
@inproceedings{73821a847c13404cbbc1b6b4ab5c4f5e,
title = "An algorithm for finding gene signatures supervised by survival time data",
abstract = "Signature learning from gene expression consists into selecting a subset of molecular markers which best correlate with prognosis. It can be cast as a feature selection problem. Here we use as optimality criterion the separation between survival curves of clusters induced by the selected features. We address some important problems in this fields such as developing an unbiased search procedure and significance analysis of a set of generated signatures. We apply the proposed procedure to the selection of gene signatures for Non Small Lung Cancer prognosis by using a real data-set.",
author = "Pagnotta, {Stefano M.} and Michele Ceccarelli",
year = "2011",
month = "9",
day = "29",
doi = "10.1007/978-3-642-23851-2_58",
language = "English",
isbn = "9783642238505",
volume = "6881 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "568--578",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 1",

}

TY - GEN

T1 - An algorithm for finding gene signatures supervised by survival time data

AU - Pagnotta, Stefano M.

AU - Ceccarelli, Michele

PY - 2011/9/29

Y1 - 2011/9/29

N2 - Signature learning from gene expression consists into selecting a subset of molecular markers which best correlate with prognosis. It can be cast as a feature selection problem. Here we use as optimality criterion the separation between survival curves of clusters induced by the selected features. We address some important problems in this fields such as developing an unbiased search procedure and significance analysis of a set of generated signatures. We apply the proposed procedure to the selection of gene signatures for Non Small Lung Cancer prognosis by using a real data-set.

AB - Signature learning from gene expression consists into selecting a subset of molecular markers which best correlate with prognosis. It can be cast as a feature selection problem. Here we use as optimality criterion the separation between survival curves of clusters induced by the selected features. We address some important problems in this fields such as developing an unbiased search procedure and significance analysis of a set of generated signatures. We apply the proposed procedure to the selection of gene signatures for Non Small Lung Cancer prognosis by using a real data-set.

UR - http://www.scopus.com/inward/record.url?scp=80053158818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053158818&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-23851-2_58

DO - 10.1007/978-3-642-23851-2_58

M3 - Conference contribution

SN - 9783642238505

VL - 6881 LNAI

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

SP - 568

EP - 578

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

ER -