Self-organizing latent lattice models for temporal gene expression profiling

Byoung Tak Zhang, Jinsan Yang, Sung Wook Chi

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)67-89
Number of pages23
JournalMachine Learning
Volume52
Issue number1-2
DOIs
Publication statusPublished - 2003 Jul

Keywords

  • Correlated genes
  • DNA-microarray data
  • Learning latent-variable models
  • Temporal expression profiling
  • Visualization

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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