Hierarchical Feature Selection for Knowledge Discovery: Application of Data Mining to the Biology of Ageing

by Cen Wan

Overview

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 providesthe 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.

Product Details

ISBN-13:	9783319979182
Publisher:	Springer International Publishing
Publication date:	11/30/2018
Series:	Advanced Information and Knowledge Processing
Edition description:	1st ed. 2019
Pages:	120
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Dr. Cen Wan is a Postdoctoral Research Associate in the Department of Computer Science at University College London, and in the Biomedical Data Science Laboratory at The Francis Crick Institute, London, UK.

Table of Contents

Introduction.- Data Mining Tasks and Paradigms.- Feature Selection Paradigms.- Background on Biology of Ageing and Bioinformatics.- Lazy Hierarchical Feature Selection.- Eager Hierarchical Feature Selection.- Comparison of Lazy and Eager Hierarchical Feature Selection Methods and Biological Interpretation on Frequently Selected Gene Ontology Terms Relevant to the Biology of Ageing.- Conclusions and Research Directions.