Retinal photoreceptor cells are responsible for light detection and phototransduction. The understanding of molecular mechanisms regulating photoreceptor gene expression during retinal development may have important implications in clinical neuroscience. Using self-adaptive neural networks and pattern validation statistical tools, this paper explores large-scale analysis of photoreceptor gene expression. Based on the analysis of data generated by serial analysis of gene expression (SAGE) in the developing mouse retina, significant expression patterns for the in silico detection of photoreceptor-enriched genes were revealed. This study demonstrates how machine learning and statistical techniques may be effectively combined to detect key complex relationships encoded in SAGE data. Such approaches may support inexpensive functional predictions prior to the application of experimental methodologies.