A key challenge of gene expression time series research is the development of efficient and reliable probabilistic models. In response, we propose an unsupervised conditional random, fields (CRFs) model for gene expression time series clustering. Conditional random fields have demonstrated superior performance over generative models such as hidden Markov models (HMMs) in terms of computational efficiency on many sequenee-data-based tasks. Yet their potential has not been previously explored in this field. In the proposed model, time series data are allowed to interact with each other via a voting pool scheme while clusters are progressively formed. Experiments based on both biological data and simulated data verify the suitability of our model to gene expression data analysis via the comparison with a recent work.
|Title of host publication||Proceedings of the 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007|
|Number of pages||6|
|Publication status||Published - 2007|
|Event||2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007 - Cairns, Australia|
Duration: 21 Feb 2007 → 23 Feb 2007
|Conference||2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007|
|Period||21/02/07 → 23/02/07|