Fundamentals of Data Mining in Genomics and Proteomics [Dubitzky, Granzow & Berrar 2006-12-19](1).pdf

FUNDAMENTALS OF DATA MINING IN

GENOMICS AND PROTEOMICS

FUNDAMENTALS OF DATA MINING IN

GENOMICS AND PROTEOMICS

Edited by

Werner Dubitzky

University of Ulster, Coleraine, Northern Ireland

Martin Granzow

Quantiom Bioinformatics GrmbH & Co. KG, Weingarten/Baden, Germany

Daniel Berrar

University of Ulster, Coleraine, Northern Ireland

Springer

Library of Congress Control Number: 2006934109

ISBN-13: 978-0-387-47508-0

e-ISBN-13: 978-0-387-47509-7

ISBN-10: 0-387-47508-7

e-ISBN-10: 0-387-47509-5

Printed on acid-free paper.

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Preface

As natural phenomena are being probed and mapped in ever-greater detail,

scientists in genomics and proteomics are facing an exponentially growing vol-

ume of increasingly complex-structured data, information, and knowledge. Ex-

amples include data from microarray gene expression experiments, bead-based

and microfluidic technologies, and advanced high-throughput mass spectrom-

etry. A fundamental challenge for life scientists is to explore, analyze, and

interpret this information effectively and efficiently. To address this challenge,

traditional statistical methods are being complemented by methods from data

mining, machine learning and artificial intelligence, visualization techniques,

and emerging technologies such as Web services and grid computing.

There exists a broad consensus that sophisticated methods and tools from

statistics and data mining are required to address the growing data analysis

and interpretation needs in the life sciences. However, there is also a great deal

of confusion about the arsenal of available techniques and how these should

be used to solve concrete analysis problems. Partly this confusion is due to

a lack of mutual understanding caused by the different concepts, languages,

methodologies, and practices prevailing within the different disciplines.

A typical scenario from pharmaceutical research should illustrate some of

the issues. A molecular biologist conducts nearly one hundred experiments

examining the toxic effect of certain compounds on cultured cells using a

microarray gene expression platform. The experiments include different com-

pounds and doses and involves nearly 20 000 genes. After the experiments are

completed, the biologist presents the data to the bioinformatics department

and briefly explains what kind of questions the data is supposed to answer.

Two days later the biologist receives the results which describe the output of

a cluster analysis separating the genes into groups of activity and dose. While

the groups seem to show interesting relationships, they do not directly address

the questions the biologist has in mind. Also, the data sheet accompanying

the results shows the original data but in a different order and somehow trans-

formed. Discussing this with the bioinformatician again it turns out that what

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