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Hierarchical Feature Selection for Knowledge Discovery

Application of Data Mining to the Biology of Ageing

  • Cen Wan

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

About this book

Introduction

This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

Keywords

Bioinformatics Hierarchical Feature Selection Gene Ontology Biology of Ageing Data Mining Knowledge Discovery

Authors and affiliations

  • Cen Wan
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-97919-9
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-97918-2
  • Online ISBN 978-3-319-97919-9
  • Series Print ISSN 1610-3947
  • Series Online ISSN 2197-8441
  • Buy this book on publisher's site