Chapter title |
High-Dimensional Profiling for Computational Diagnosis.
|
---|---|
Chapter number | 12 |
Book title |
Bioinformatics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6613-4_12 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6611-0, 978-1-4939-6613-4
|
Authors |
Claudio Lottaz, Wolfram Gronwald, Rainer Spang, Julia C. Engelmann, Lottaz, Claudio |
Editors |
Jonathan M. Keith |
Abstract |
New technologies allow for high-dimensional profiling of patients. For instance, genome-wide gene expression analysis in tumors or in blood is feasible with microarrays, if all transcripts are known, or even without this restriction using high-throughput RNA sequencing. Other technologies like NMR finger printing allow for high-dimensional profiling of metabolites in blood or urine. Such technologies for high-dimensional patient profiling represent novel possibilities for molecular diagnostics. In clinical profiling studies, researchers aim to predict disease type, survival, or treatment response for new patients using high-dimensional profiles. In this process, they encounter a series of obstacles and pitfalls. We review fundamental issues from machine learning and recommend a procedure for the computational aspects of a clinical profiling study. |
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