An artificial neural network integrated pipeline for biomarker discovery using Alzheimer's disease as a case study

Zafeiris, D., Rutella, S. ORCID: 0000-0003-1970-7375 and Ball, G.R. ORCID: 0000-0001-5828-7129, 2018. An artificial neural network integrated pipeline for biomarker discovery using Alzheimer's disease as a case study. Computational and Structural Biotechnology Journal, 16, pp. 77-87. ISSN 2001-0370

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Abstract

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

Item Type: Journal article
Publication Title: Computational and Structural Biotechnology Journal
Creators: Zafeiris, D., Rutella, S. and Ball, G.R.
Publisher: Research Network of Computational and Structural Biotechnology
Date: 2018
Volume: 16
ISSN: 2001-0370
Identifiers:
NumberType
10.1016/j.csbj.2018.02.001DOI
S2001037017300843Publisher Item Identifier
Rights: © 2018 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 05 Jul 2018 07:52
Last Modified: 02 Aug 2018 08:36
URI: http://irep.ntu.ac.uk/id/eprint/34007

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