title: Assessing the information content of short time series expression data.
abstract: Due to experimental constraints, the sampling of biological system with microarray data is severely constrained. In similar fashion to sampling theory of signals, the under-sampling of a system oftentimes leads to sub-optimal results from which it is difficult to draw proper conclusions. In our work we create a mathematical framework which will show that the sampling methodology for short time series microarray data may lead to data whose ability to distinguish non-random behavior within the biological system is severely constrained.
authors.affiliation: Rutgers University, Piscataway, NJ 08845, USA.
authors.forename: Eric H
authors.initials: EH
authors.lastname: Yang
authors.forename: Ioannis P
authors.initials: IP
authors.lastname: Androulakis
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015870:Gene Expression; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D012984:Software; D013997:Time Factors