  This report explores the application of machine learning techniques on short
timeseries gene expression data. Although standard machine learning algorithms
work well on longer time-series', they often fail to find meaningful insights
from fewer timepoints. In this report, we explore model-based clustering
techniques. We combine popular unsupervised learning techniques like K-Means,
Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the
well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture
Models are fairly standard, while Hidden Markov Model and Bayesian Networks
clustering are more novel ideas that suit time-series gene expression data.
