Chapter 2: Responsive Gene Discovery

A phenomenon change will usually occur in a species after an appropriate stress has been applied. What researchers want to know is how an observed phenomenon change in a species is determined by or associated with some genetic function. It has been well-researched that it is the genetic function of a species that responds to a stress directly and leads to the observed phenomenon change. Rather than employing all of genetic elements, such as genes, a biological system only activates a small proportion of genes to respond to a stress. Moreover, different genes may have different strengths to respond to a stress. Filtering out non-responsive genes to discover responsive genes thus plays a key role in researching and investigating the mechanism by which a biology system responds to a stress. From this, interpreting, explaining and extrapolating the discovered mechanism can follow. No doubt, computational models are required to help discover these responsive genes through a research of the pattern by which a phenomenon change is associated with genetic functions. And the unique function of the computational models constructed by machine learning algorithms can never be ignored and neglected for this purpose. A task of responsive gene discovery normally has no a priori knowledge about how such a pattern is formed, and hence discovered. This therefore fits to one typical machine learning mechanism, i.e., unsupervised learning. This chapter will introduce several unsupervised machine learning algorithms such as density estimation and cluster analysis and demonstrate how these algorithms can be used for responsive gene discovery. A typical responsive gene discovery problem, i.e., the essential gene discovery problem, will be introduced and discussed in this chapter. How essential genes can be discovered using unsupervised machine learning algorithms will be demonstrated.