title: High-Dimensional Profiling for Computational Diagnosis.
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.
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. Claudio.Lottaz@klinik.uni-regensburg.de.
authors.forename: Claudio
authors.initials: C
authors.lastname: Lottaz
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: Wolfram
authors.initials: W
authors.lastname: Gronwald
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: Rainer
authors.initials: R
authors.lastname: Spang
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: Julia C
authors.initials: JC
authors.lastname: Engelmann
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D000069550:Machine Learning; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
keywords: Feature selection; Gene expression profiles; Metabolite analysis; Microarrays; Model assessment; NMR finger printing; RNA sequencing; Statistical classification; Supervised machine learning