Challenges in the analysis of mass-throughput data: a technical commentary from the statistical machine learning perspective.
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.
Aliferis
Constantin F
CF
Discovery Systems Laboratory, Department of Biomedical Informatics, Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA. constantin.aliferis@vanderbilt.edu
Statnikov
Alexander
A
Tsamardinos
Ioannis
I