An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study
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...
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doaj-9e8490d8fff142bebc3fd776f69f988c2020-11-25T02:25:56ZengElsevierComputational and Structural Biotechnology Journal2001-03702018-01-01167787An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case StudyDimitrios Zafeiris0Sergio Rutella1Graham Roy Ball2Corresponding author.; John van Geest Cancer Research Centre, College of Science and Technology, Nottingham Trent University, United KingdomJohn van Geest Cancer Research Centre, College of Science and Technology, Nottingham Trent University, United KingdomJohn van Geest Cancer Research Centre, College of Science and Technology, Nottingham Trent University, United KingdomThe 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. Keywords: Artificial neural network, Machine learning, Supervised learning, Network inference, Alzheimer's disease, Biomarker discoveryhttp://www.sciencedirect.com/science/article/pii/S2001037017300843 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dimitrios Zafeiris Sergio Rutella Graham Roy Ball |
spellingShingle |
Dimitrios Zafeiris Sergio Rutella Graham Roy Ball An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study Computational and Structural Biotechnology Journal |
author_facet |
Dimitrios Zafeiris Sergio Rutella Graham Roy Ball |
author_sort |
Dimitrios Zafeiris |
title |
An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study |
title_short |
An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study |
title_full |
An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study |
title_fullStr |
An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study |
title_full_unstemmed |
An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study |
title_sort |
artificial neural network integrated pipeline for biomarker discovery using alzheimer's disease as a case study |
publisher |
Elsevier |
series |
Computational and Structural Biotechnology Journal |
issn |
2001-0370 |
publishDate |
2018-01-01 |
description |
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. Keywords: Artificial neural network, Machine learning, Supervised learning, Network inference, Alzheimer's disease, Biomarker discovery |
url |
http://www.sciencedirect.com/science/article/pii/S2001037017300843 |
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