Algorithms for clustering data
Algorithms for clustering data
GTM: the generative topographic mapping
Neural Computation
Learning in graphical models
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Evolutionary induction of sparse neural trees
Evolutionary Computation
Circular SOM for temporal characterisation of modelled gene expressions
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
DNA hypernetworks for information storage and retrieval
DNA'06 Proceedings of the 12th international conference on DNA Computing
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DNA microarrays are a high-throughput technology useful for functional genomics and gene expression analysis. While many microarray data are generated in sequence, most expression analysis tools are not utilizing the temporal information. Temporal expression profiling is important in many applications, including developmental studies, pathway analysis, and disease prognosis. In this paper, we develop a learning method designed for temporal gene expression profiling from massive DNA-microarray data. It attempts to learn probabilistic lattice maps of the gene expressions, which are then used for profiling the trajectories of temporal expressions of co-regulated genes. This self-organizing latent lattice (SOLL) model combines the topographic mapping capability of self-organizing maps and the generative property of probabilistic latent-variable models. We empirically evaluate the SOLL model on a set of cell-cycle regulation data, demonstrating its effectiveness in discovering the temporal patterns of correlated genes and its usefulness as a tool for generating and visualizing interesting hypotheses.