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The abundance of a significant portion of the temporal induction-repression expression pattern of a gene among other genes in a time-course data is an indication of its non-randomness. The significance of the portions that match between two gene profiles can be derived using binomial analysis or its variant. Considering the induction-repression pattern alone is both meaningful and significant since the related genes induced/repressed in a given period may not show the same exact shape of induction/repression. Further, microarray measurements are of low quality, which might make expression patterns of related genes less similar. Based on this observation we developed an algorithm called friendly neighbors (FNs). This algorithm finds the significance score of a gene as the number of genes in the same experiment that share its induction-repression pattern more than a certain threshold. The concept of friendly neighbors is different from that of nearest neighbors. A neighbor that satisfies certain similarity condition is called friendly neighbor where as a nearest neighbor is the most similar neighbor of all neighbors. This leads to the observation that all friendly neighbors does not necessarily be nearest neighbors, vice versa. The FNs approach has been applied to discover putative estrogen target genes and to detect cell cycle regulated genes in S. cerevisiae. The new approach performed better than paired t-test and simple expression level based filtering methods on estrogen target gene discovery. It did significantly well on cell cycle regulated gene discovery in the absence of task-specific knowledge. Its performance is better than commonly used Fourier transform method and fold change methods. Apart from detecting cell cycle regulated genes, it also detected other prominent patterns which could be detected only by more complicated clustering and data analysis methods. Availability: http://giscompute.gis.a-star.edu.sg/FNs.