This book is designed to introduce biologists, clinicians and
computational researchers to fundamental data analysis principles,
techniques and tools for supporting the discovery of biomarkers and
the implementation of diagnostic/prognostic systems.
The focus of the book is on how fundamental statistical and data
mining approaches can support biomarker discovery and evaluation,
emphasising applications based on different types of ‚omic‘ data.
The book also discusses desig...
This book is designed to introduce biologists, clinicians and
computational researchers to fundamental data analysis principles,
techniques and tools for supporting the discovery of biomarkers and
the implementation of diagnostic/prognostic systems.
The focus of the book is on how fundamental statistical and data
mining approaches can support biomarker discovery and evaluation,
emphasising applications based on different types of ‚omic‘ data.
The book also discusses design factors, requirements and techniques
for disease screening, diagnostic and prognostic applications.
Readers are provided with the knowledge needed to assess the
requirements, computational approaches and outputs in disease
biomarker research. Commentaries from guest experts are also
included, containing detailed discussions of methodologies and
applications based on specific types of ‚omic‘ data, as well as
their integration. Covers the main range of data sources currently
used for biomarker discovery
* Covers the main range of data sources currently used for
biomarker discovery
* Puts emphasis on concepts, design principles and methodologies
that can be extended or tailored to more specific applications
* Offers principles and methods for assessing the
bioinformatic/biostatistic limitations, strengths and challenges in
biomarker discovery studies
* Discusses systems biology approaches and applications
* Includes expert chapter commentaries to further discuss
relevance of techniques, summarize biological/clinical implications
and provide alternative interpretations