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Authors: Maysson Al-Haj Ibrahim 1 ; Joanne L Selway 1 ; Kian Chin 2 ; Sabah Jassim 1 ; Michael A. Cawthorne 1 and Kenneth Langlands 1

Affiliations: 1 Buckingham University, United Kingdom ; 2 Milton Keynes Hospital NHS Foundation Trust, United Kingdom

ISBN: 978-989-758-012-3

Keyword(s): Disease Classification, Breast Cancer, Prognosis, Biomarkers, Metabolic Networks and Pathways, Gene Regulatory Networks, Microarray Analysis.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Pattern Recognition, Clustering and Classification ; Transcriptomics

Abstract: Accurate prognostication is central to the management of breast cancer, and traditional clinical and histochemical-based assessments are increasingly augmented by genetic tests. In particular, the use of microarray data has allowed the creation of molecular disease signatures for the early identification of individuals at elevated risk of relapse. However, tailoring therapy on the basis of a molecular assay is only recommended in certain cases, and the identification of a minimal set of genes whose expression allows informed decision-making in a broader spectrum of disease remains challenging. Finding an optimal solution is, however, an intractable computational task (i.e. retrieving the smallest group of genes with the greatest prognostic power). Our solution was to reduce the genetic search-space by using two filtering steps that enriched by biological function those genes whose expression discriminated disease states. In this way, we were able to identify a new molecular signature, the expression characteristics of which facilitated the classification of intermediate risk disease. We went on to create a statistical test that confirmed the relevance of our approach by comparing the performance of our signature to that of 1000 random signatures. (More)

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Paper citation in several formats:
Al-Haj Ibrahim, M.; L Selway, J.; Chin, K.; Jassim, S.; A. Cawthorne, M. and Langlands, K. (2014). Rational Identification of Prognostic Markers of Breast Cancer.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 265-270. DOI: 10.5220/0004915202650270

@conference{bioinformatics14,
author={Maysson Al{-}Haj Ibrahim. and Joanne L Selway. and Kian Chin. and Sabah Jassim. and Michael A. Cawthorne. and Kenneth Langlands.},
title={Rational Identification of Prognostic Markers of Breast Cancer},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={265-270},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004915202650270},
isbn={978-989-758-012-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Rational Identification of Prognostic Markers of Breast Cancer
SN - 978-989-758-012-3
AU - Al-Haj Ibrahim, M.
AU - L Selway, J.
AU - Chin, K.
AU - Jassim, S.
AU - A. Cawthorne, M.
AU - Langlands, K.
PY - 2014
SP - 265
EP - 270
DO - 10.5220/0004915202650270

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