  We propose a similarity measure for sparsely sampled time course data in the
form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP
similarity is similar to a Bayes factor and provides enhanced robustness to
noise in sparse time series, such as those found in various biological
settings, e.g., gene transcriptomics. We show that the GP measure is equivalent
to the Euclidean distance when the noise variance in the GP is negligible
compared to the noise variance of the signal. Our numerical experiments on both
synthetic and real data show improved performance of the GP similarity when
used in conjunction with two distance-based clustering methods.
