Assessing the information content of short time series expression data.
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.
Yang
Eric H
EH
Rutgers University, Piscataway, NJ 08845, USA.
Androulakis
Ioannis P
IP
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