Time-series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time-series data available today consist of few time points only, thus making the application of standard clustering techniques difficult. In this talk, first, I will summarize the qualitative characteristics of the approaches that are specifically designed to mine short time-series gene expression data. Then I will talk about two new algorithms that are capable of extracting biological significant patterns from short time-series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Next, I will explore some biological application of ASTRO and MiMeSR using three different short time-series gene expression dataset: Saccharomyces cerevisiae response to stress by amino acid starvation, seed development in Brassica napus, and a well defined ovarian cancer gene expression dataset representing distinct stages of the disease. Finally, I will discuss the main challenges that this type of complex data present, and explore the opportunities in the context of developing novel approaches.