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An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study.

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.

journal_name

Comput Struct Biotechnol J

journal_title

Computational and structural biotechnology journal

authors

Zafeiris D,Rutella S,Ball GR

doi

10.1016/j.csbj.2018.02.001

subject

Has Abstract

pub_date

2018-02-21 00:00:00

pages

77-87

issn

2001-0370

pii

S2001-0370(17)30084-3

journal_volume

16

pub_type

杂志文章,评审

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