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Motivation: Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. Due to the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clustering algorithms are unable to distinguish between real and random patterns. Results: We present andoi:10.1093/bioinformatics/bti1022 pmid:15961453 fatcat:5dzg2tcb2zcnhoeg2jivv3ofvy