nts are undertaken genome wise and it is the so called big data

data thus needs novel methods to deal with. In gene essentiality

iscovery based on the transposon sequencing technology, the

of an individual mutant has been replaced by the massive analysis

ons of mutants simultaneously. Therefore, the conventional

of discovering essential genes have been challenged and have

laced by the big-data essential gene discovery methods. This

as shown how unsupervised machine learning approaches can be

responsive gene discovery, in which there is normally no a priori

ge about how to separate responsive genes from non-responsive

Various density estimation approaches and cluster analysis

ms have been introduced and demonstrated in association with the

ject of this chapter, i.e. responsive gene discovery. In addition,

iples, the working procedures and the technical implementations

e been introduced as well. However, the gene essentiality pattern

y still needs more substantial improvement. One important issue,

as not yet been fully solved, is the uncertainty of transposon

on resulting from various early-stage data preparation errors,

mple preparation, to sequencing and to alignment. An error in

p may cast an accuracy or an effectiveness problem in the gene

ty pattern discovery for a data set. How to properly incorporate

ertainties for a better gene essentiality pattern discovery certainly

re effort.