Challenges in the analysis of mass-throughput data: A technical commentary from the statistical machine learning perspective

Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations

Abstract

Sound data analysis is critical to the success of modern molecular medicine research that involves collection and interpretation of mass-throughput data. The novel nature and high-dimensionality in such datasets pose a series of nontrivial data analysis problems. This technical commentary discusses the problems of over-fitting, error estimation, curse of dimensionality, causal versus predictive modeling, integration of heterogeneous types of data, and lack of standard protocols for data analysis. We attempt to shed light on the nature and causes of these problems and to outline viable methodological approaches to overcome them.

Original languageEnglish (US)
Pages (from-to)133-162
Number of pages30
JournalCancer Informatics
Volume2
DOIs
StatePublished - 2006

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