loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Julio De Alejandro Montalvo ; George Panoutsos ; Mahdi Mahfouf and James W. Catto

Affiliation: The University of Sheffield, United Kingdom

ISBN: 978-989-8565-35-8

Keyword(s): Feature-selection, Neural-fuzzy, Fuzzy Logic, Radial-Basis-Function (RBF), Microarray, Bladder Cancer.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Data Mining and Machine Learning ; Databases and Data Management ; Soft Computing

Abstract: This paper introduces a Fuzzy entropy-based method for the problem of feature selection. For the first time Fuzzy-Entropy is used to directly link the relative input relevance of a Radial-Basis-Function Neural-Fuzzy modelling structure. This embedded feature selection method uses the model performance as a criterion for the feature selection. The resulting model maintains its simplicity and transparency in the form of a linguistic Fuzzy-Logic rule-base. The proposed methodology is validated using a real biomedical case-study, which concerns the signature selection for the identification of the stage of bladder cancer. The signature selection and predictive modelling results are compared to previous research work on the same dataset, and it is shown that the RBF-NF model outperforms the previous modelling attempts by achieving high predictive accuracy (>90%). The model is shown to maintain its good performance even when using just 10 genes in the gene based signature.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.200.4

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
De Alejandro Montalvo, J.; Panoutsos, G.; Mahfouf, M. and Catto, J. (2013). Radial Basis Function Neural-fuzzy Model for Microarray Signature Identification.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013) ISBN 978-989-8565-35-8, pages 134-139. DOI: 10.5220/0004226801340139

@conference{bioinformatics13,
author={Julio De Alejandro Montalvo. and George Panoutsos. and Mahdi Mahfouf. and James W. Catto.},
title={Radial Basis Function Neural-fuzzy Model for Microarray Signature Identification},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)},
year={2013},
pages={134-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004226801340139},
isbn={978-989-8565-35-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)
TI - Radial Basis Function Neural-fuzzy Model for Microarray Signature Identification
SN - 978-989-8565-35-8
AU - De Alejandro Montalvo, J.
AU - Panoutsos, G.
AU - Mahfouf, M.
AU - Catto, J.
PY - 2013
SP - 134
EP - 139
DO - 10.5220/0004226801340139

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.